{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Neural Nets\n",
    "\n",
    "This type of neural nets are predominantly (and heavily) used in image processing.\n",
    "\n",
    "## Useful terms:\n",
    "\n",
    "1. Convolution\n",
    "2. Max pooling\n",
    "2. Softmax\n",
    "3. Cross Entropy\n",
    "\n",
    "## Further Readings:\n",
    "https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/BcEapJEKz3M?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/BcEapJEKz3M?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tqdm\n",
      "  Downloading tqdm-4.14.0-py2.py3-none-any.whl (46kB)\n",
      "\u001b[K    100% |################################| 51kB 1.5MB/s ta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: tqdm\n",
      "Successfully installed tqdm-4.14.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from urllib.request import urlretrieve\n",
    "from os.path import isfile, isdir\n",
    "from tqdm import tqdm\n",
    "# import problem_unittests as tests\n",
    "import tarfile\n",
    "import pickle\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Conv2D, MaxPool2D, Flatten, BatchNormalization\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cifar10_dataset_folder_path = 'cifar-10-batches-py'\n",
    "\n",
    "class DLProgress(tqdm):\n",
    "    last_block = 0\n",
    "\n",
    "    def hook(self, block_num=1, block_size=1, total_size=None):\n",
    "        self.total = total_size\n",
    "        self.update((block_num - self.last_block) * block_size)\n",
    "        self.last_block = block_num\n",
    "\n",
    "if not isfile('cifar-10-python.tar.gz'):\n",
    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n",
    "        urlretrieve(\n",
    "            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n",
    "            'cifar-10-python.tar.gz',\n",
    "            pbar.hook)\n",
    "\n",
    "if not isdir(cifar10_dataset_folder_path):\n",
    "    with tarfile.open('cifar-10-python.tar.gz') as tar:\n",
    "        tar.extractall()\n",
    "        tar.close()\n",
    "        \n",
    "label_dict = dict(zip(range(10), \n",
    "                      ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']))  \n",
    "width = height = 32\n",
    "channels = 3\n",
    "train_examples = 50000\n",
    "\n",
    "# Get the test set\n",
    "with open(cifar10_dataset_folder_path + '/test_batch', mode='rb') as file:\n",
    "    batch = pickle.load(file, encoding='latin1')\n",
    "\n",
    "    test_x = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)/255\n",
    "    test_y = batch['labels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_batch(batch_size):\n",
    "    n_batches = 5\n",
    "    while(1):\n",
    "        for batch_id in range(1, n_batches + 1):\n",
    "            with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file:\n",
    "                batch = pickle.load(file, encoding='latin1')\n",
    "\n",
    "                features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)\n",
    "                labels = batch['labels']\n",
    "                for start in range(0, len(features), batch_size):\n",
    "                    end = min(start + batch_size, len(features))\n",
    "                    yield features[start:end]/255, np.array(labels[start:end])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c42500ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/YzPrBfBtAEOotev6iLuHsw3qVKt5TE+QZJYqfx/KWkdwfGSEr6Aeeuk4tbVk\n+Ip+UzdfZV9O2oOtXt5N52QiCUt93X3UFsk9Qj7HD3N/f1hBWLM6vDoMAOeGh6nt4MED1DZU3EBt\nTImZmOCv2fQ0X0kfv8JVk9hqf6UYTqxKN/MknP37eKu3WAut/v6V1LbmVl4LsX9FeN7yFbzuYgvx\n/7G/fZzOmc18rvwFAP/U3d+CWjvue83sdgAPAXjM3TcDeKz+vxDiDcKcwe81Xn1rzdZ/HMD9AB6u\njz8M4AOL4qEQYlGY13d+M0vXO/SeB/Couz8NYKW7v9pKdhgA/8wjhLjhmFfwu3vF3XcAWAtgl5nd\nPMvuIN2BzexBM9tjZnsmJkghDyFEw7mq1X53HwPwOIB7AYyY2QAA1H+fJ3N2u/tOd9/Z2clvqRRC\nNJY5g9/MVphZT/3vVgDvBfAygEcAfLz+sI8D+MFiOSmEWHjmk9gzAOBhM0uj9mbxHXf/oZk9CeA7\nZvYJACcAfGTOLVUdVdJ2KRV5H8qUwkkpXaT1FwA8+9TPqW14hCfGWJYnueza9bbg+J137KRzrlzh\n0tbeXz9NbVN5nshy8OQpajt6/HhwPDfNv3K58yJ4LV08uWR8fILaJkhLsalxLlNGSvEhk+bW7sgn\nytUbwnLksr4BOqd/NZfYVt92C7X1Rmr4NcVqQzJbJBkLHo6XVKRl2GzmDH533wvgtsD4KIC7570n\nIcQNhe7wEyKhKPiFSCgKfiESioJfiISi4BciodjV1Py67p2ZXUBNFgSA5QC45tY45MdrkR+v5Y3m\nx3p35/rsDBoa/K/Zsdked+cCufyQH/JjUf3Qx34hEoqCX4iEspTBv3sJ9z0T+fFa5Mdr+Qfrx5J9\n5xdCLC362C9EQlHwC5FQliT4zexeM3vFzA6b2ZIV/jSz42b2opk9b2Z7Grjfr5nZeTPbN2Os18we\nNbND9d+8Ed7i+vE5MztTPybPm9l9DfBj0MweN7OXzGy/mX26Pt7QYxLxo6HHxMxazOwZM3uh7sd/\nrI8v7PFw94b+AEgDOAJgI4AmAC8A2N5oP+q+HAewfAn2+y4AbwWwb8bYfwPwUP3vhwD81yXy43MA\n/n2Dj8cAgLfW/+4EcBDA9kYfk4gfDT0mqJU26Kj/nQXwNIDbF/p4LMWVfxeAw+5+1N2LAL6FWiXg\nxODuTwC4NGu44dWQiR8Nx93Pufuv639PADgAYA0afEwifjQUr7HoFbOXIvjXAJhZiuY0luAA13EA\nPzGzZ800bWFKAAABiUlEQVTswSXy4VVupGrInzKzvfWvBYv+9WMmZjaEWvGYJa0QPcsPoMHHpBEV\ns5O+4Hen16oSvx/AJ83sXUvtEBCvhtwAvozaV7IdAM4B+EKjdmxmHQC+C+Az7v6aFj2NPCYBPxp+\nTPw6KmbPl6UI/jMABmf8v7Y+1nDc/Uz993kA30ftK8lSMa9qyIuNu4/UT7wqgK+gQcfEzLKoBdw3\n3P179eGGH5OQH0t1TOr7vuqK2fNlKYL/7wBsNrMNZtYE4KOoVQJuKGbWbmadr/4N4H0A9sVnLSo3\nRDXkV0+uOh9EA46JmRmArwI44O5fnGFq6DFhfjT6mDSsYnajVjBnrWbeh9pK6hEAf7hEPmxETWl4\nAcD+RvoB4JuofXwsobbm8QkAfaj1PDwE4CcAepfIj78A8CKAvfWTbaABftyJ2kfYvQCer//c1+hj\nEvGjoccEwK0Anqvvbx+A/1AfX9Djodt7hUgoSV/wEyKxKPiFSCgKfiESioJfiISi4BcioSj4hUgo\nCn4hEsr/AwmU/m7ZA1RAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c3057cb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ESqJZfRWtoJAPW3q5vF3A8+ChsJV233/fY/Z59Cc/MWOitn01NG7bPMOHjwbbs7bDg6KT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\nPAXg6STHAeAbqL5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c304ea048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OL0SiZLra73A4iSIpk+AdgK+wdlZ5frlynq/Ae5HbeiR4BwByufAYo++gkbJQd9xxF7Wx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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c303f3c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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J6CI1/GLJQD932xZuUwLwq+5+ORrtuG8ws6sA3ArgAXffCeCB5v9CiNcJcwa/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\nvxCJouAXIlEU/EIkioJfiERR8AuRKP8fHfrYoWBq62kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c305287b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = next(get_batch(5))\n",
    "for im, label in zip(x,y):\n",
    "    plt.imshow(im)\n",
    "    plt.title(label_dict[label])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic logistic multiclass classification:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 1000\n",
    "gen = get_batch(batch_size)\n",
    "x_train, y_train = next(gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logistic = LogisticRegression()\n",
    "logistic.fit(x_train.reshape(batch_size,-1), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 1, 8, 0, 4, 3, 3, 6, 1, 1])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = logistic.predict(test_x.reshape(len(test_x), -1))\n",
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting the probabilities for the first 3 images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5.11780589e-02,   5.22601998e-02,   1.55926385e-01,\n",
       "          1.91240503e-01,   3.91236477e-04,   1.54297967e-01,\n",
       "          2.80945273e-01,   1.19926488e-06,   1.13659809e-01,\n",
       "          9.93687013e-05],\n",
       "       [  2.16303715e-02,   7.31044039e-01,   2.69961983e-03,\n",
       "          1.05058501e-01,   4.19521439e-04,   4.01069801e-03,\n",
       "          7.30693763e-06,   2.05908231e-04,   2.05482815e-02,\n",
       "          1.14375752e-01],\n",
       "       [  6.08332707e-02,   8.16219690e-02,   7.55828475e-03,\n",
       "          1.26261683e-01,   2.92891682e-02,   9.79890820e-03,\n",
       "          7.44308146e-03,   4.27092877e-03,   6.19882712e-01,\n",
       "          5.30399941e-02]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic.predict_proba(test_x[:3].reshape(3,-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy of the predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.275"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(y_pred == test_y)/len(test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of parameters for a fully connected network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30720"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "32*32*3*10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Keras Multilayered Perceptron (Neural Net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_batch(batch_size):\n",
    "    n_batches = 5\n",
    "    while(1):\n",
    "        for batch_id in range(1, n_batches + 1):\n",
    "            with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file:\n",
    "                batch = pickle.load(file, encoding='latin1')\n",
    "\n",
    "                features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)\n",
    "                labels = batch['labels']\n",
    "                for start in range(0, len(features), batch_size):\n",
    "                    end = min(start + batch_size, len(features))\n",
    "                    x = features[start:end]/255\n",
    "                    y = labels[start:end]\n",
    "                    yield x.reshape(len(x),-1), np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to note that when we do classification problems we use the **Categorical Crossentropy Loss**. When its only two classes we can use Logistic Loss (Binary Crossentropy Loss). Finally for regression problems we use **Mean Squared Error**.\n",
    "\n",
    "The Cross Entropy loss is defined as:\n",
    "$$\\mathcal{L} = -\\frac{1}{N}\\sum_i \\mathcal{I}(y_i=1)\\log(p_{i1})+\\mathcal{I}(y_i=2)\\log(1-p_{i2})+\\cdots++\\mathcal{I}(y_i=K)\\log(1-p_{iK})$$\n",
    "where $N$ is the number of training instances, $K$ is the number of classes and $p_{ik}$ is the probability that instance $i$ belongs to $k$.\n",
    "\n",
    "Softmax takes a $D$ dimensional vector and squeezes them through a function such that we have $D$ outputs whos values are positive and sums to one.\n",
    "$$\n",
    "\\text{softmax}(\\mathbf{y})_d = \\frac{\\exp(-y_d)}{\\exp(-y_1)+...+\\exp(-y_D)}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 Hidden Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 9, 9, 4, 1])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "0 0 0 0 0 0 1 0 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 100)               307300    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 308,310.0\n",
      "Trainable params: 308,310\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(units=100, input_dim=width*height*channels, activation='relu'))\n",
    "model.add(Dense(units=10, activation='softmax'))\n",
    "model.compile(optimizer='adadelta', loss='sparse_categorical_crossentropy')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "195/195 [==============================] - 3s - loss: 2.0425     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f9096696f28>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 256\n",
    "model.fit_generator(get_batch(batch_size=batch_size), train_examples//batch_size, epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9728/10000 [============================>.] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.3412"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict_classes(test_x.reshape(len(test_x),-1))\n",
    "np.count_nonzero(y_pred == test_y)/len(test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convolution Neural Networks (CNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Points to note **\n",
    "1. One CNN, connected to **one** node above is simply a Dense layer with most weights set to zero.\n",
    "2. The same CNN, connected to multiple nodes is weight tying/ sharing.\n",
    "\n",
    "Consider the following convolution mask:\n",
    "<img src='https://ujwlkarn.files.wordpress.com/2016/07/screen-shot-2016-07-24-at-11-25-24-pm.png?w=74&h=64'>\n",
    "<img src='https://ujwlkarn.files.wordpress.com/2016/07/convolution_schematic.gif?w=536&h=392'>\n",
    "\n",
    "![](cnn.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_batch(batch_size):\n",
    "    n_batches = 5\n",
    "    while(1):\n",
    "        for batch_id in range(1, n_batches + 1):\n",
    "            with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file:\n",
    "                batch = pickle.load(file, encoding='latin1')\n",
    "\n",
    "                features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)\n",
    "                labels = batch['labels']\n",
    "                for start in range(0, len(features), batch_size):\n",
    "                    end = min(start + batch_size, len(features))\n",
    "                    x = features[start:end]/255\n",
    "                    y = labels[start:end]\n",
    "                    yield x, np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the max pooling layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(8, kernel_size=(3,3), padding='same', activation='relu', input_shape = (width,height,channels)))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(16, kernel_size=(3,3), padding='same', activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(32, kernel_size=(3,3), padding='same', activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "model.compile(optimizer='adadelta', loss='sparse_categorical_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 8)         224       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 16)        1168      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 16)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 8, 8, 32)          4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 11,162.0\n",
      "Trainable params: 11,162.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "195/195 [==============================] - 31s - loss: 2.0763    \n",
      "Epoch 2/15\n",
      "195/195 [==============================] - 32s - loss: 1.7705    \n",
      "Epoch 3/15\n",
      "195/195 [==============================] - 34s - loss: 1.6121    \n",
      "Epoch 4/15\n",
      "195/195 [==============================] - 37s - loss: 1.5431    \n",
      "Epoch 5/15\n",
      "195/195 [==============================] - 37s - loss: 1.5011    \n",
      "Epoch 6/15\n",
      "195/195 [==============================] - 36s - loss: 1.4731    \n",
      "Epoch 7/15\n",
      "195/195 [==============================] - 32s - loss: 1.4511    \n",
      "Epoch 8/15\n",
      "195/195 [==============================] - 31s - loss: 1.4312    \n",
      "Epoch 9/15\n",
      "195/195 [==============================] - 33s - loss: 1.4157    \n",
      "Epoch 10/15\n",
      "195/195 [==============================] - 34s - loss: 1.4000    \n",
      "Epoch 11/15\n",
      "195/195 [==============================] - 36s - loss: 1.3849    \n",
      "Epoch 12/15\n",
      "195/195 [==============================] - 36s - loss: 1.3716    \n",
      "Epoch 13/15\n",
      "195/195 [==============================] - 33s - loss: 1.3587    \n",
      "Epoch 14/15\n",
      "195/195 [==============================] - 31s - loss: 1.3452    \n",
      "Epoch 15/15\n",
      "195/195 [==============================] - 31s - loss: 1.3333    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f9093673d30>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 256\n",
    "model.fit_generator(get_batch(batch_size=batch_size), train_examples//batch_size, epochs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9920/10000 [============================>.] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5234"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict_classes(test_x)\n",
    "np.count_nonzero(y_pred == test_y)/len(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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79uwJ+nxYNK7qX63Mhg9v+pAhcPrKAb7URrxvXHXfq1b53o8tDlP6a8Tj9s/J33u8b2tr\na8Uxizh7APh6LUuTzwIkLwfweQA3mNnhuF9EZCrSmzERqYWNAFaSvJBkK4CbAaz3O5BcDuArAD5g\nZs+Pcg4RkSmp7m/GRGTyMbM8yTsAPAwgC+BeM9tM8vak/x4AfwhgDoDPJm9Z82bW06gxi4g0C03G\nRKQmzGwDgA3RZ/e49ocAfKje4xIRaXZ1n4z5HKeurq5yO86L8ttHjhwJ+vx2fJxfuqivry/o83lo\ncX6Vz/2KS2n4XCl/jli8/FK8r88hi5dx8ks8xblZPqcr7vP3Eeeo+Xyvassoxc/Cl93wZT60HJKI\niEjtKWdMREREpIE0GRMRERFpIE3GRERERBqorjljxWIxqG/ll+HxuWSn9j0lzgvzeUxxnpTP94qX\nNfJLLk2fPj3o6+zsLLdnzZpV8TifWxZfw+erjXZ9f6xfZggIa5vFS0P5ZxHnofkcsvb29op9/rmP\ndp5K4/T3VK3emYiIiJwdvRkTERERaSBNxkREREQaqK5hynw+j0OHDpW3jx8/Xm7H4T8fcotLTfjw\nX7yUj++Lw3bvete7yu1qSwfFJSp8KY0DBw4EfX6pJl+eAjg93OjvNw5T+rIR1cKy8bh9qDcue+Gv\nUS28Wo0PA2s5JBERkdrTmzERERGRBhpzMkZyGclvktxCcjPJjyafzyb5CMltya+zxjqXiIiIiITG\n82YsD+D3zGwVgDcC+DDJVQDWAnjUzFYCeDTZFhEREZEzMGbOmJntBbA3afeSfA7AEgCrAbw92e0+\nAN8CcGe1c+Xz+SD/ypeQiJfaOXbsmB9DPKZyOy6J4fOa4uWQ/PJLM2bMCPp86Ye9e/cGfc8991y5\nHZev2LlzZ7m9a9euoG/lypXBts+/8qUsgDBPLL5fX4Yifk7Vlmry5/G5esDpOWSV+HGqtIWIiEjt\nnVHOGMkVAK4E8DiABclEDQD2AVhQ05GJiIiITAHjnoyR7AbwZQAfM7MTvs9Kr2CswnFrSG4iuUlv\nVkRERERC4yptQbIFpYnYl8zsK8nH+0kuMrO9JBcBODDasWa2DsA6AOjq6jJfiuJtb3tbuf30008H\nx/nwX2tra9DnQ2dxaQsftjx69Gg8lnJ75syZQZ+fKMbH+ZIVcfmKOXPmjDqu0c7jS03EKwf468dl\nL3x4Nb6Gv9+BgYGgr1pVf9934kQwtw7G4o9TaQsREZHaG8+3KQngbwA8Z2Z/6rrWA7g1ad8K4Gu1\nH56IiIjI5DaeN2NvAfABAD8m+VTy2X8DcBeAB0neBmAngPedmyGKiIiITF7j+Tbl9wBUik+9o7bD\nEREREZla6rocUrFYDJY22rFjR7ntlxUCgMHBwXJ70aJFQZ/P94pLPfjlgfbt2xf0+WtfeumlQZ/P\nUfNtoPoSS52dnaO2gdNzv1555ZVy298fUL1chy/RMW/evKDPl7OolvvV3d0d9FUrHeLzxJQzJiIi\ncm5pOSQRERGRBtJkTERqguT1JLeS3E7ytBU5SF5G8gckh0h+vBFjFBFpRnUNU5pZEPL79re/XW7H\nJRvmzp1bbsehSB8qjMOGPsQXl6Hw53nyySeDPh8K9OUqAGDPnj3ldlwiwo/z4MGDqMaHA+Nq+f4+\n4jClv4+4JIYPy/oVDYCwtIZfYQAIw5TxOX040pcOUZhSKiGZBXA3gOsA7AawkeR6M9vidjsC4CMA\nbmrAEEVEmpbejIlILVwDYLuZvWhmwwAeQGnJtDIzO2BmGwGMby0uEZEpQpMxEamFJQD84qy7k89E\nRGQMmoyJSFPxS6iNFfoXEZkM6pozlslkgvIPR44cCfo8n8cU55P57bichM/LivOkfP5T3OeXXIpz\nqPy+cV7W8ePHKx4X57NVGicQ3n98Ht8Xl8Tw+WQ+fw0ARkbSaFB8v/6c8bP3+WzKE5Nx2gNgmdte\nmnx2xvwSaj09PaOueSsiMpnozZiI1MJGACtJXkiyFcDNKC2ZJiIiY6jrmzERmZzMLE/yDgAPA8gC\nuNfMNpO8Pem/h+RCAJsATAdQJPkxAKvM7ETFE4uITAF1nYyRDEKMPsQWl6/w1fK7urqCPl9ZPg7b\nebNnzw62/b7PPvts0OdDdXFpi4svvji4B8+HWuMQpg99AmEV/LiSvufLbMTX9PcOhM/Jl7KIr+9L\nigBhKDS+J38Nf/44tCrimdkGABuiz+5x7X0ohS9FRMTRmzEREZEpYMXahxo9hPPWjrtuPKfnV86Y\niIiISANpMiYiIiLSQHUPU/r8pEptICzL0NvbG/T5nK44L8vnNcXH+XPGyxF1dHSU2wsWLAj6fImK\n+DifAxfnVMX7+qWU4pIcfmwxX5IjvobP6aq2xJLPVwOAGTNmlNtxKQ3P5+udOKE8axERkVrTmzER\nERGRBtJkTERERKSB6l7awofcfHgsDrF5cQjPh8sWL14c9PnyEtXClHGpBx/Si8tO+NBgfJwPPcZl\nNuKq974MRlyGwj+XuHyF347DlP4e/fljceh12rRp5ba/ByAMTfpxbd26teL5RURE5OzozZiIiIhI\nA2kyJiIiItJAmoyJiIiINFBdc8ay2WxQUsGXd4jzwvzySHGe1MDAQLkdl7bwyxpVW+bHnwMIS0TE\nZSd8TlWc2+bz3uJcrzhnzI8nLifhS2TEyz/5ZZ38/cVj83lvQPXll/z14+P8WHzJD58/JiIiIrWh\nN2MiIiIiDaTJmIiIiEgD1TVMmclkgrCXL68Qh/R8ODAOU/rQXFwiYt++feV2XGrCl6WIw43+GvH1\n/Dh9GwhDgX19fUFfHO704cY4NOj3jUtN+FBsHMKcPn16uR2HEf01fOgRCMOU/mcSX++SSy4ptx96\nSIvMioiI1JrejImIiIg00JiTMZLtJH9I8mmSm0n+r+Tz2SQfIbkt+XXWWOcSERERkdB43owNAfhZ\nM3s9gCsAXE/yjQDWAnjUzFYCeDTZFhEREZEzMGbOmJUSqE4lQ7Uk/xmA1QDennx+H4BvAbhzHOcr\nty+66KJy2+dTAWEJh7gMhC+DEZfE8CUqYtVyxnx+VZx75fOt4uP8ckRx35IlS4Jtn18W54X568e5\nZj4v7Kqrrgr6li9fXnHcPr8sfobVSnL4sflSJHG+nIiIiEzcuHLGSGZJPgXgAIBHzOxxAAvMbG+y\nyz4ACyqeQERERERGNa7JmJkVzOwKAEsBXEPytVG/ofS27DQk15DcRHJTtbdWIiIiIlPRGZW2MLNj\nJL8J4HoA+0kuMrO9JBeh9NZstGPWAVgHAHPnzjUfcps5c2a5vXDhwvi4cjsuy+D74hCbr3Ifl4Hw\n4Tc/DgC4+OKLy20fmgPCkF4cijx8+HC5PVbI1I8trqTvw7T+ucR9S5cuDfrishSeH2t8Pd8Xj7PS\n/cYlP0RERGTixvNtynkkZybtDgDXAfgJgPUAbk12uxXA187VIEWk+ZG8nuRWkttJnvaFHpZ8Jul/\nhuRVo51HRGSqGc+bsUUA7iOZRWny9qCZfZ3kDwA8SPI2ADsBvO8cjlNEmljy58PdKP1jbTeAjSTX\nm9kWt9sNAFYm/10L4HPJryIiU9p4vk35DIArR/n8MIB3nItBich55xoA283sRQAg+QBK37j2k7HV\nAO5PckwfIznzVKpD/YcrItI86roc0uHDhw/df//9OwHMBXContc+TzT7c7mg0QOQprUEwC63vRun\nv/UabZ8lAILJGMk1ANYkm30kt9Z2qE2haX+v81ONHkFdNe3PAdDPoplM4Gcxrr836zoZM7N5AEBy\nk5n11PPa5wM9F5HwSz+TlX6vNwf9HJrHVP9ZaG1KEamFPQCWue2lyWdnuo+IyJSjyZiI1MJGACtJ\nXkiyFcDNKH3j2lsP4IPJtyrfCOC48sVEROocpnQmdQhiAvRc5LxkZnmSdwB4GEAWwL1mtpnk7Un/\nPQA2AHg3gO0A+gH8eqPG2wT0e7056OfQPKb0z4Iq5CkiIiLSOApTioiIiDSQJmMOyR0k39nocYiI\niMjUUdfJ2FjLpUwVJJeR/CbJLSQ3k/xo8vlsko+Q3Jb8OqvRYxWR6pLitb9do3O9neTXa3GuqYLk\nTSRXneNrrCD5bIW+z5+6fvIP+rnncizno0rPzz+7MY6f9L8v6jYZc8ul3ABgFYBbzvVvoEYgOZ4v\nReQB/J6ZrQLwRgAfTp7FWgCPmtlKAI8m2yLS3GYCOG0yNs4/C2TibkLp75SGMLMPRct+yThVenbJ\nfGFKqeebsfJyKWY2DODUcinN5opkEePjJP+BZDsAkPzN5I3eEZLrSS4+dQBJI/lhktsAbEu+uv9n\nJA+QPEHyxyRfm+zbBuD3APwzyf0APg1gK0qVyFcDuC857X0o/SEjIs3tLgAXk3yK5EaS3yW5HsCW\n+I0AyY+T/ETSvoTkv5F8muSTJC/2JyV5NckfxZ9PBST/meQTSeRgTfJZn+t/L8kvkHwzgJ8H8Onk\n+V9M8gqSjyV/jn/1VISB5LeSP5c3kXwueb5fSSIRf+LO/bskn03++5gbVo7kl5Jj/4lkpzvvacVK\nSf4nkj9MxvVXU3GCETnt+flnR7KP5P8m+TSANyWRtJ+QfBLALzZ26OdePSdjlZZCaTbvA3A9gAsB\nXA7g10j+LIBPJn2LUFoY/YHouJtQWv5lFYCfA/A2AJcCmJEcdzjZ767k8ysAXJL89zYAjwNY4Oou\n7QOwoPa3JyI1thbAC2Z2BYDfB3AVgI+a2aVjHPclAHeb2esBvBluWahkknEPgNVm9sK5GXZT+w0z\newOAHgAfITlntJ3M7Pso1a/7fTO7InlW9wO408wuB/BjAP/THTKcVHm/B8DXAHwYwGtR+nN+Dsk3\noFRy5VqUoha/SfLU2syvAvBZM3s1gBMY5W3oKSRfDeBXALwl+f+iAOD9Z/MgJpGxnl8XgMeT3w+b\nAPw1gPcAeAOAhfUcaCMogf90nzGzV8zsCIB/QWnS9H6U6iY9aWZDAP4rSjP3Fe64T5rZETMbADAC\nYBqAy1AqH/Kcme0lSZTW3Pu/k/MbSlXIB83shB9Espiy6o6InH9+aGYvVduB5DQAS8zsqwBgZoNm\n1p90vxqlmkvvMbOXz+1Qm9ZHkjckj6G0asPK8RxEcgaAmWb27eSj+1D6x+4ppwoR/xjAZjPbm/yZ\n/mJynbcC+KqZnTSzPgBfAfBTyTG7zOw/kvYXk30reQdKk4iNJJ9Kti8azz1MYmM9vwKALyftywC8\nZGbbkr8Lv1inMTZMPXMazpelUPa5dj+AxQDmAHjy1Idm1kfyMEpv9nYkH+9y/f9O8i9RypG7gORX\nAHwcQDuATgBPlOZl6EIpf6yQHLqf5KJk4rYIwIHa356InGMnXTuP8B+97eM4fm+y35UAXqnhuM4L\nJN8O4J0A3mRm/SS/hdLz8P84Hc9zHM1Q8mvRtU9tj/X3YfyP42r/WCaA+8zsv57Z8Ca1sZ7foJkV\nMEXV883YeJZLaVavwK28TrILpQman0wG/2OZ2WeS1+yrUApL/j5KK9IPAHgNSvd+t5l1mFl3cth6\nALcm7VtReo0uIs2tF6U34aPZD2B+EgJrA/B/AYCZ9QLYTfImoJRLeioHCcAxADcC+GQyMZlqZgA4\nmkzELkMpXAiU/rH6apIZAL/g9i8/fzM7DuAoyVNvsz4A4NsYv+8CuCnJZ+pKrvPdpG85yTcl7V8F\n8L0q53kUwHtJzgfK35S/oMr+U8GZPL+fAFjh8iVvOacjawJ1m4yZWR7AqeVSngPwoJltrtf1J+jv\nAfx6khjaBuD/RSm2vWO0nZPE0GtJtqD0r+RBAEUzK6IUB/87lP6Q+NkkQfUFku9GKZ/sOpa+CPDO\nZFtEmpiZHQbwH0mi/qejvhEAfwTghwAeQekvmVM+gFI47hkA34fLizGz/ShN3O4mee25vYOm839Q\nSvZ+DqU/Ax9LPl8L4OsoPSu/pukDAH7ffdnhVpQS+p9BKc3kj8Z7YTN7EsAXUPp5PQ7g82b2o6R7\nK0rffH8OwCwAn6tyni0A/gDAN5JxPIJSvvFUdibPbxCllJ6HkgT+SR8l0nJIDskdAD5kZv+WbH8C\nwCVm9p9YWmPv91H6n+j7AG43s93JfgZgpZltT7bfAeDPUMoRGERpAvqfk/BmO4A/ROnN4FyU3q59\nzsw+U7c0U20xAAAgAElEQVQbFRERkaahyZiIiIhIA+nblCIiIiINpMmYiIiISANpMiYiIiLSQBOa\njFELf4uIiIhMyFkn8CfrbD0P4DqUljbaCOCWagumTp8xw+YvSFc1OHrsWNpZDMcxNDhQbp8+RpZb\nM2bMCHrMlfvq6+0N+lpaWsrtfH4k6BsZSbdLFSjCs5avzHD+mhRvBQB0dIR1CDs7O4PtbDZXsa9Q\nSK9ZLIbX9/v6eyiN1Y3NPZdw1KdvedGjD65fdJ17du/CkSOHw4uInENz5861FStWNHoYIiJn5Ykn\nnjhkZvPG2m8iFfjLC38DAMlTC39XnIzNX7AQf3r3Z8vb//SVtKZpcTAf7PvC8+lphvNDQR/dsG+4\n4cagz9xE4jvffDToW7Q4Xerx8OGwbMm+/Wn91qHh/qDP3ISkta016GtrTbdfvWpV0HfVlVcF27Nn\nzy23X//6K4O+48fS1ZAGB8P7veqq9Dzz5oU/Uz9ZykTr0PpJVaEYFzZO51RD+XDy1z+QXr//ZNq+\n6eevg0g9rVixAps2bWr0MEREzgrJnePZbyJhynEt/E1yDclNJDedOH4s7hYRERGZ0s55Ar+ZrTOz\nHjPrmT5j5rm+nIiIiMh5ZSJhyjNe+LursxNveEMacjvicsaefPzJYN8fnThabo8UB4O+6dPSPLHH\nHv9WeA2Xt3XwcPh2cN+BF9INxjlq6TXI8LFkXJ4YEYYCW1rayu2XX34x6OvtDd8EXrB8Zbn9nW8/\nFvS51DMsXhyumjGST0O4F0b5Mx0dHeV2Z2dX0Dd9+nQ3zvCe+vvT+z18NMyta2tPz5nJqCiwiIjI\nuTSRN2Pn88LfIiIiIk3hrN+MmVme5KmFv7MA7j2PFv4WkUlsxdqHGj2E89aOu24ceycRqamJhClh\nZhsAbBjv/keOHsGD//gPbvt4uX34UPjtxpt+cXW5PX/BrKDvoYfSP2gPH9kf9J08mb7sa+sIupDL\npSHMllxYImJoKO0bCateID+Sd33DQd/goIsvRt9mbG9rC7Znzkxz5k72HQz62ty3NKdNmxb0PfP0\n0+X2ls3hfDeXS3+E7e3h9S666KJy+8orw29vdnWl1zh25GjQN39++mxmzUz3y2VVI1hERKTW9Ler\niIiISANpMiYiIiLSQJqMiYiIiDTQhHLGztThw4dx//1fKG+3tqfL/CxbENaLvfaaa8vthYvDqvOP\nPZaWhcgXwhyugf40Dy2XC8sy5LJpflc+H1b892bN6g62/VJJI1FCma9y394eLnE0Y8acitdYvGR+\n+IG5ivhDYQX+SssTAcCIW9apf6Av6Nv3g73l9p49u4K+iy66pNzOZsKSGB3taf7c7NlpeQxqISQR\nEZGa05sxERERkQbSZExERESkgeoapuzq6kRPT095+0dPPVNuHzgQlrb44hf/vtw+eGhf0Hf4SLov\nM+Ei10OD6YLbsDCk2NWZlp5obQ1DioViGu7sO3ki6BseTsOGXV1hSK8t58pJWDi3PXI4LBkxfVoa\n8ps+PSxfkc2k5STiUCjdCgDZTBgr7OpK63fE4376qXRVg5d37gj61v/L18vta3veFvQtW5aGjDc9\n/r1y++iRIxAREZHa0psxERERkQbSZExEaoLk9SS3ktxOcm2V/a4mmSf53nqOT0SkWWkyJiITRjIL\n4G4ANwBYBeAWkqsq7PcpAN+o7whFRJpXXXPGOto7ccVrripvd7akJSQuf93lwb5P/+ipcrs7Wh5o\n2vQ0b2v3rheCvumdM8rtTDTXbO9I86vmLwjLZYy4JY8OHzoc9B0bSnOl8kNhSYz2ljRnbOHscNmm\nWdPCEhnZ4TQvrSsTjo3FQrndYmEenF8e6cChMG9rx8s7yu3jx8Jx73x+W7n9cktr0MdsmqO2YN5P\ngr49u9PtIy+9lJ7/SHh+EecaANvN7EUAIPkAgNUAtkT7/Q6ALwO4ur7DExFpXnozJiK1sASAL2a3\nO/msjOQSAL8A4HPVTkRyDclNJDcdPHiw2q4iIpOCJmMiUi9/DuBOs+jVb8TM1plZj5n1zJs3r9qu\nIiKTQl3DlLlsFjNdeYefe8c7y+1FixYG+86ZlYYbn9j4eNBnQ73ldueSZUFfRy4Nv3W0dAR9uZZ0\n7tk9LezLF9K/HxbPCMONx46nocHBwbDK/eLFi8rt+TPDqvojg+HqAIVCGuI8tGt31JeGKdvaw7Gd\nPJGWrNh38FDQt3t3ep5iISyJsXhh+kyPHDsW9Pn7LxT6g749u/eU28P70jIi+ajkhoizB4D/zbg0\n+czrAfAAS0s5zAXwbpJ5M/vn+gxRRKQ51XUyJiKT1kYAK0leiNIk7GYAv+p3MLMLT7VJfgHA1zUR\nExHRZExEasDM8iTvAPAwgCyAe81sM8nbk/57GjpAEZEmpsmYiNSEmW0AsCH6bNRJmJn9Wj3GJCJy\nPqjrZKy3txff/fa/l7ez2XR5opkzZwb7dnSmpRheen5z0Nd3YH+5PaOtLejrnpbmjE3vCG+vtSXd\nbo++unDU5WUV+06G57Q0n2vZgjC3LZdJ76F3f7ik08mTA8G2z1v2OWKlE6Vj29Ub5pMd703HMzQS\nltbo7U+v0RWV0rjiNWmZp507tgV9flWl40fDb6x1dbslllzJjSIMIiIiUlv6NqWIiIhIA2kyJiIi\nItJAdQ1TDg72Y8uWZ8rbPT095XZ3d3uwb19vWu290B+WZegopqG6XD6cT46MDJbb/cWWoK9QTEOK\ngyejcF/f0XS/qMo+kcb0RvrD0GPvQHq94aGw9EOxGIb1fJiyaGFf54w0TNveFo67ry/dd2R4KOiD\nCyPmh8K+fa+k4c5XrVwZ9E2b1llub929I+hr7Uh/Fr1uv0xGc3cREZFa09+uIiIiIg2kyZiIiIhI\nA2kyJiIiItJAdc0Zy2Qy6O5M85Ge/8mWcnvXznAJoNZcmn811Hsk6JuGNKcq18Kgb6iYHpc/GS5d\nlOlM859yxbC0RDGf5om1trcGfWT6mAoWXg+ZdCzMxMsFhdfID7ux5cO8tNaR9LnMn7cg6BtyuWBH\n42WNOtLSHjNndAV9J46lz23JW94U9M2emy75tLc3LG1BV2ajw5W5yGQ1dxcREak1/e0qIiIi0kBj\nTsZI3kvyAMln3WezST5Cclvy66xq5xARERGR0Y0nTPkFAH8J4H732VoAj5rZXSTXJtt3jnkmMxQL\nw+XNoYG0XRgZDnZtZVoyopNhGYhMIQ33jbhyFQCQz6e3NHI0LPXQOZKepyUX3jqHXHX86Km0uSr/\nPmQJAC1ZF9JkGHrM94fXJ9NrMBveUz6f3n8uE4ZCh4fTPkZV8Ls70jDpcP+JoA8t6dhybWHpkLbO\ntFq/RSHb/Eg6zoHh9OcQl+MQERGRiRvzzZiZfQfAkejj1QDuS9r3AbipxuMSERERmRLONoF/gZnt\nTdr7ACyotCPJNQDWAEB7e1ul3URERESmpAkn8JuZAZVXkDazdWbWY2Y9rS0tlXYTERERmZLO9s3Y\nfpKLzGwvyUUADozrKBqy2TTfK9eRXn5osBjsmnfLDk3vDPPCchm3r0VLF1l6zqKFc01flWLQLZtU\n6nR5U+Ep4a9OhOOEy+/K58JxxudpdWMrFMLzDLucueN9Ye7XiYGTbjDhSXOt6biL2fD6Q+4aR48e\nDfpmz5tbbveeCK83NOJy3fz8OarqIeKRvB7AX6D0W+bzZnZX1L8awB8DKKL0u+NjZva9ug9URKTJ\nnO2bsfUAbk3atwL4Wm2GIyLnI5JZAHcDuAHAKgC3kFwV7fYogNeb2RUAfgPA5+s7ShGR5jSe0hZ/\nD+AHAF5FcjfJ2wDcBeA6ktsAvDPZFpGp6xoA283sRTMbBvAASl/0KTOzviStAQC6UCW9QURkKhkz\nTGlmt1ToeseZXqytNYcLVqS5/sND6Z/FL72wP9h3eCgN241EuWYtrkRENiptQRfCbGmLjnPV6vMD\nYbgv05ru29oeloHIuPicFcPwYsFtF6PQIxnOdfO+fEf011CmkF6j70S4csBQPg3ZLlw+J+hbOH92\neg6GX5A4fiINNx45cjjoe23768rtefNnBn1HXeX+jo70eq1PvwSRCpYA2OW2dwO4Nt6J5C8A+CSA\n+QBuHO1E/ks/y5cvr/lARUSajSrwi0jdmNlXzewylMrh/HGFfcpf+pk3b159Bygi0gCajIlILewB\nsMxtL00+G1VSv/AiknMr7SMiMlVoMiYitbARwEqSF5JsBXAzSl/0KSN5CUkm7asAtAE4fNqZRESm\nmLMtbXFWMlmie5orZ+FWEioUw6WDii4vbKgQ5nf5nDEWw7ywolvap627K+jLtaUXPHH8WNCXLaZJ\nXB2t4TmZSeeshZGRoM9c8lcmE+WIWZhDdqI/zQVrbwvzu7IuZ+zggbBSyPzFaU7XBZcsDPqC0h75\nsPbE3M70uJHhsJTHyztfLLc7u8L/DYbyaR5ehumzyGQ1d5fRmVme5B0AHkaptMW9ZraZ5O1J/z0A\nfgnAB0mOABgA8CsuoV9EZMqq62RMRCYvM9sAYEP02T2u/SkAn6r3uEREmp1edYiIiIg0UF3fjBWt\niIGh3vL20ABdXyHc10XcMlHYsDCclnoYGBgI+oqtaWiwlWGY0ocJj/WF5SPamT6K9igs2taSlroo\nFsJQ4OBwGrbMMuyLw6sDhXTftqgMRV9/+lyQC0Oh8xfOKLeHC+H9mguFMgqLmiu1MTwUHvfslifK\n7dbuKERcTEtwWFCeQxElERGRWtObMREREZEG0mRMREREpIE0GRMRERFpoLrmjBGAr/7gv9ReLIb5\nVj6DLBsta4R8muM0NBCWbBhypSf69g0HfW2daQ5ZIfpG/cmh9Jwdg+E54xIV3uBQum9LlDPGqBSE\nv4+RfJQjl023Zy+eHvQVMuk1RgbCseSyLp+N4T3R0mdRRPgsinlXkiMfLSnl8sTCygPKGRMREak1\nvRkTERERaSBNxkREREQaqL5hShJtLR3l7XzOlaHIhKGyERfis0wY/su7KvuDw2FZhs55c8rt7oWz\ng77B4bTURN9QWD4i78pQnOg7EfS1j6Rjzkahx/6Bk+V2MQo95tpbg+1MLn3c+cEwbDhtfhpCnTY3\nLMmBbPqcsohWB3AlOeLn5Ifa2h2OxYrpOaNqHSi4Ph+mVK10ERGR2tObMREREZEG0mRMREREpIE0\nGRMRERFpoPouFG5AYSRNPKJbAqklG+ZwFQppLhhzYamHlo40b2pm68ygr3Ved7ndviBccqh1JL3d\nYibM7+o9kuZJ9Q72Bn2DebfkUTZMnMq65ZfY1h709Y+EJTK6ZqT3kc31B30tnel5c61h/pyZ+zFF\nOWvmylkQ4TM0pMlgw+HKTKCrMVKMSlb4Uh4+t0wpYyIiIrWnN2MiIiIiDaTJmIiIiEgD1TdMKSKT\nFsnrAfwFgCyAz5vZXVH/+wHcidJiHL0AfsvMnq77QKWuVqx9qNFDOG/tuOvGRg9B6qSuk7GiFTFY\n6CtvF8wVuMpEeUuFNP8p1xLmUM2cPavcbsmG9bMGOtJcsAJPBn25tvR6cxaG+V3Tpqe1xAb6wlwv\ns/QFYktL+DLRH9ffF3Rh6HB4ns5pneX2YLTkUq4tvUdG9cLM5XvFtb58HbBMNnxOwX5RPlmhmJ6U\nDI8Lnre5cUXLPYmcwtL/RHcDuA7AbgAbSa43sy1ut5cA/LSZHSV5A4B1AK6t/2hFRJqLwpQiUgvX\nANhuZi+a2TCABwCs9juY2ffN7Giy+RiApXUeo4hIU9JkTERqYQmAXW57d/JZJbcB+NfROkiuIbmJ\n5KaDBw/WcIgiIs2pvjljGQNa03BZfsiVXiiGtReMaUmFTDYKj7lwWYHRfNLtmo2KMZgr2RAf1jEt\nDVu2Tw9DmD4UWCyGJTHMnWg4Hy5x1N4RhlCLmfQe+/NhmLLdqoQwXSiW0cAzbjmk+KaYKbqtcNwj\nebeMVBR+LPqlkgrpcf75iZwtkj+D0mTsraP1m9k6lEKY6OnpUUUVEZn0lMAvIrWwB8Ayt700+SxA\n8nIAnwdwg5kdrtPYRESa2phhSpLLSH6T5BaSm0l+NPl8NslHSG5Lfp011rlEZNLaCGAlyQtJtgK4\nGcB6vwPJ5QC+AuADZvZ8A8YoItKUxpMzlgfwe2a2CsAbAXyY5CoAawE8amYrATyabIvIFGRmeQB3\nAHgYwHMAHjSzzSRvJ3l7stsfApgD4LMknyK5qUHDFRFpKmOGKc1sL4C9SbuX5HMoJeauBvD2ZLf7\nAHwLpRpCVfnsJLqlfAqFsPQCgvykMFdpcNiVr4j6Wjumlds5C5dDyhfTnK7hQrSskZuWZlqjtYNc\nvlUxSl+zYlr6oVAMz9neFV6/6M6T7QzzyVo70xIZmWjJo2zW/ZgsHEDB5XcVC2FeGN3STcUo36vo\n8uBGhsMcNc/ny1lcV0PEMbMNADZEn93j2h8C8KF6j0tEpNmd0bcpSa4AcCWAxwEsSCZqALAPwIIK\nx5S/GTXQPzLaLiIiIiJT1rgnYyS7AXwZwMfM7ITvs9Irk1Ffm5jZOjPrMbOejs6W0XYRERERmbLG\n9W1Kki0oTcS+ZGZfST7eT3KRme0luQjAgTHPY0Qmn17Shl15hagsRMaFHy0qy9DRlYb09h3qDfo6\nh9PQYC4fzjVHCgPpOaNq9SO+kv1IGNLLZn0oMOhCBmkZjL6TA0HftDBKiba29INsVPaivTMtbVGw\nOGTrmlGo0HyYluHgii6mWoziqxmk189lw2dfcDcZhpUhIiIiNTaeb1MSwN8AeM7M/tR1rQdwa9K+\nFcDXaj88ERERkcltPG/G3gLgAwB+TPKp5LP/BuAuAA+SvA3ATgDvOzdDFBEREZm8xvNtyu8hjFZ5\n76jtcERERESmlrpW4LeiYaTfLa/jcrMKQ2HeUs4FUI8fC/PCpi1I86taovIR5vKtCnGCF9M+RvlV\nWb+UUFT2wgouL8vCLyEUXBWMeNmm9vZw3+HhdOcRC68/7MpuFC0qreH2LcRd9GONSlswzYuLVzLy\nJTEsFy83lR7nx6LCFiIiIrWnhcJFREREGkiTMREREZEGqm+Y0iyoEk9z5SWi6vUdnWnJiMGTYWzu\nUG9a5mz63O6grz2XlmwYLoThTXO3m7GwtEQmk/blGI6lUEjDeBl2BH29J9P7aWkJ57a5bPh4827u\nO2tueJ7BfDpWY1RaI5OGYoeK4bOgq8gfhFoB5HLpfTATnjPj7nE4KntBV8PCVM9CRETknNKbMRER\nEZEG0mRMREREpIE0GRMRERFpoLrmjAFA0S3nE6SJZcLcJJ9ulYnyyfKuDETBopIYuTTXLB/lhRVc\nyYoMwrITtHReyjhnLJ/maQ0ODwZ9Ay5nrLUjepzR0kWZoNRElIvlymIwyv0yV1Qi1xpew0ZcXpiF\n5ywWfZ5YWPZipJCW+bBMNG53mpwfF0RERKTW9GZMRGqC5PUkt5LcTnLtKP2XkfwBySGSH2/EGEVE\nmlHd34yJyOTDUoXhuwFcB2A3gI0k15vZFrfbEQAfAXBTA4YoItK06lzaAhhxpS0KLvyYjcpCmKuQ\n39qWDfp8mYaR/FB4nD8nw+r8IyNpiDEflbL3Ib1MVM4hl0sfky/NAQBHjx0ptxd0zgnHGQX28kPp\nNYaHw3FPn+1DqmEZCh9+jCKoKLh984WofEUQJo3OSX/O8Flk4Pv08lTG5RoA283sRQAg+QCA1QDK\nkzEzOwDgAMkbGzNEEZHmpDdjIlILSwDsctu7AVx7NiciuQbAGgBYvnz5xEcmIgCAFWsfavQQzls7\n7jq3/4bUaw8RaSpmts7MesysZ968eY0ejojIOafJmIjUwh4Ay9z20uQzEREZQ31zxmBBXpPP7+qc\n1hnsm3V5Yp3sCs+TS/O2hobC3KuCSxObNn1m0Hfk4I5yu7UtWg7J5VBlclFJjHyUqOXMnDmj3G7J\nhHPbof6wDMbxo347zIPrnpYuj1QcCfPSgswzxrlfWdeOS1uk58mGlwvz4Cw8p/kctaI/UMUtpKKN\nAFaSvBClSdjNAH61sUMSETk/KGdMRCbMzPIk7wDwMEr/0rjXzDaTvD3pv4fkQgCbAEwHUCT5MQCr\nzOxExROLiEwBmoyJSE2Y2QYAG6LP7nHtfSiFL0VExKl7aQtffcFcuYXu6WEo0heon901PzxPa1qK\n4dCJ/UFf7/H+cnvGjPC4eXMWltsHDrwS9GVd2K5QCGN6RUur1eejEKKv+J+Nw5TDI8F2Sy6Noc6Y\nFpbB6O5Ib/h43+Ggz1z1/EwmCplm022zyuFURmPz+44MhWFK/zPKZdOVCqqcXkRERM6SEvhFRERE\nGkiTMREREZEG0mRMREREpIHqnsDv8478ckG5XJinNVJIdyxEyUo5utIThXA+2T+c5oy9sjcsczRz\n5rRyOy5tUXGQCMtetLSEjyw/kuaFDQ6GZTYYlaHwhw4MhV8ga8+npT0yiJd/Su8xPmchn24XC/G4\n0+MyuXDcRV9ipBCVrHAlR/w5/LJMIiIiUht6MyYiIiLSQJqMiYiIiDRQXcOUBJANSlukc8G4CnzO\nTRP7Bo8FfXO70xIVXe3dQV+/9abH9YclIvqH0vPkorBdi9tubY3KQLg6G/l8PuhryblQK6My91E5\nCWTSkOZQfiAc20BaviKXCcdWyPvSFlEI09LtTFx7wm3aSOUfdaYYhVOz6T1l3S1QUUoREZGa05sx\nERERkQYaczJGsp3kD0k+TXIzyf+VfD6b5CMktyW/zjr3wxURERGZXMbzZmwIwM+a2esBXAHgepJv\nBLAWwKNmthLAo8m2iIiIiJyBMXPGrLRuTl+y2ZL8ZwBWA3h78vl9AL4F4M7qZyMycR7VqesUw8/z\n+eG0XYxKRrglgWbPnh30FY+kx424cwBA0eVGtbaFuVeZICEqXMYIdKUespXXBPJlIACgiDDJypfF\naOtsD/qyTPuK+fAadGOLc8aKrgxFNnq0LS3pUkZxrps/J6NkMP+c8oW8+1zrIYmIiNTauHLGSGZJ\nPgXgAIBHzOxxAAvMbG+yyz4AC87RGEVEREQmrXFNxsysYGZXAFgK4BqSr436DcF391Ik15DcRHLT\n0NDIaLuIiIiITFlnVNrCzI6R/CaA6wHsJ7nIzPaSXITSW7PRjlkHYB0AzJ0zzXzozOJSDE7WxdxG\nRsJJ3NGj6aXaokr6re782VwYfmNQSb8l6PNjKUThTR/ii8OsWRc2ZBRCtEJYMmK84rChv2b8zMLx\nVK49EZ/Tb+ei51RwFfnzw2lZjWo/LxERETk74/k25TySM5N2B4DrAPwEwHoAtya73Qrga+dqkCLS\n/EheT3Irye0kT/tCD0s+k/Q/Q/KqRoxTRKTZjOfN2CIA95HMojR5e9DMvk7yBwAeJHkbgJ0A3ncO\nxykiTSz58+FulP6xthvARpLrzWyL2+0GACuT/64F8LnkVxGRKW0836Z8BsCVo3x+GMA7zsWgROS8\ncw2A7Wb2IgCQfAClb1z7ydhqAPcnOaaPkZx5KtWh/sMVEWkedV0O6fCRvkN/+8Xv7QQwF8Chel77\nPNHsz+WCRg9AmtYSALvc9m6c/tZrtH2WAAgmYyTXAFiTbPaR3FrboTaFpv29zk81egR11bQ/B0A/\ni2YygZ/FuP7erOtkzMzmAQDJTWbWU89rnw/0XETCL/1MVvq93hz0c2geU/1nobUpRaQW9gBY5raX\nJp+d6T4iIlOOJmMiUgsbAawkeSHJVgA3o/SNa289gA8m36p8I4DjyhcTEalzmNKZ1CGICdBzkfOS\nmeVJ3gHgYQBZAPea2WaStyf99wDYAODdALYD6Afw640abxPQ7/XmoJ9D85jSPwuqkKeIiIhI4yhM\nKSIiItJAmoyNgeQOku8c5fOfOtOv3JP8Ask/qd3oRERE5HxX18nYWMulnE/M7Ltm9qqzOZbkMpLf\nJLmF5GaSH00+n03yEZLbkl9n1XbUIlJrJFeQfLbR45DKSH6C5McbPY6piuRHSD5H8kuNHkuzqttk\nzC2XcgOAVQBuIbmqXtevJ5JjfTEiD+D3zGwVgDcC+HDyLNYCeNTMVgJ4NNkWkUlqHH9WSJPQz2pC\nfhvAdWb2/lMf6HmG6vlmrLxcipkNAzi1XMr54OrkLdZRkn9Lsp3k20nuPrVDEs68k+QzAE6SzJG8\nkuSTJHtJ/gOAdgAws71m9mTS7gXwHEqVyFcDuC855X0AbqrnTYrIWcuS/OvkTfc3SHaQvILkY8mi\n6F899aab5LdI/jnJTQA+SvKXST5L8mmS30n2yZL8NMmNyfH/uaF3dx4i+d9JPk/yewBelXx2Mcn/\nQ/IJkt8leVny+TySX06e90aSb0k+/wTJvyP5HwD+rnF3c/4ieQ+AiwD8K8nj/nkmf5f+Lckfk/wR\nyZ9Jjukk+WDy9+5XST5OclIXhK3nZKzSUijng/cDeBeAiwFcCuAPKux3C4AbAcxE6dn+M0q/gWcD\n+EcAvxQfQHIFSmt/Pg5ggau7tA/AglrdgIicUysB3G1mrwFwDKXf6/cDuNPMLgfwYwD/0+3famY9\nZva/AfwhgHeZ2esB/HzSfxtKddiuBnA1gN8keWGd7uW8R/INKNW6uwKlcipXJ13rAPyOmb0BwMcB\nfDb5/C8A/FnyvH8JwOfd6VYBeKeZ3VKPsU82ZnY7gFcA/AyAP0P4PD9c2sVeh9Lfn/eRbEfpTdrR\nJHr0PwC8oSGDryO9JhyfvzSzXQBA8v8B8P8B+LdR9vuM2+9tAFoA/HmyMPI/kfxdvzPJbgBfBvAx\nMztBstxnZkZSdUdEzg8vmdlTSfsJlP7hNtPMvp18dh9K/yA75R9c+z8AfIHkgwC+knz2cwAuJ/ne\nZHsGShO+l87F4CehnwLwVTPrBwCS61GKTLwZwD+6P2vbkl/fCWCV+3x68uczAKw3s4G6jHpq8M/z\nrSj9fQoz+wnJnSi98HgrShNkmNmzScRpUqvnZOx8XgrFv9HbCWDxOPZbDGCPhYXcdp5qkGxBaSL2\nJQcVtTIAACAASURBVDM79QfwfpKLzGwvyUUADkx86CJSB0OuXUDp7Xg1J081zOx2ktei9Fb9ieSt\nDlF6g/NwzUc6dWUAHDOzKyr0vdHMBv2HyeTs5Cj7y9nT8xxFPcOU41kupVn5SeRylF65jsZPvPYC\nWEL/uqt0LJLP/gbAc2b2p65/PYBbk/atAL42kUGLSMMcB3CU5E8l2x8A8O3RdiR5sZk9bmZ/COAg\nSn/ePAzgt5J/tIHkpSS76jDuyeI7AG5KcvemAXgPSqs+vETyl4HSn8MkX5/s/w0Av3PqYJKjTdik\n9r6LUhoQSF6K0t+RW1F6W/y+5PNVAF7XqAHWS90mY2aWB3BquZTnADxoZpvrdf0J+jDJpSRnA/jv\nCEMMlfwApW9NfoRkC8lfROlLDADwFpT+cP5Zkk8l/70bwF0AriO5DaXX5nfV/E5EpF5uBfDpJMRy\nBYA/qrDfp5ME5mcBfB/A0yjlLG0B8GTy+V9BaSXjlnxB6h9Qepb/itLLAKD0F/9tJJ8GsBnpl8g+\nAqAn+bLEFgC313nIU9VnAWRI/hiln9evmdlQ8vm85GfxJyj9rI43bpjnnpZDGgPJHSj9QfgBlEKP\nXwPwWyhNrL5oZkvdfh8ys39zx/YA+GsAl6C0Lh8AbDOzSl8AEBERmdJYKoXVYmaDJC9GKUf7VUkl\nhklJkzERERFpGklo+ZsofQmOKH0r+V8bO6pzS5MxERERkQbS2pQiIiIiDaTJmIiIiEgDTWgyxkm0\n8LeIiIhII5x1zljybYfnAVyH0tJGGwHcYmZbKh2TyWQsk0m/nR1U4Dr9Cq5deYyMTuLvp9qdVb92\n5WtUO44IO8l4ruvGVmVw1X4mcV9wv1WOi5/TeK/nb7hYyKNYLJ7BkxOZmLlz59qKFSsaPQwRkbPy\nxBNPHDKzeWPtN5G6NeWFvwGA5KmFv6tMxnKYOWOu207/Xs9ms+HObhKQL+bD87hJTi46bqRQSI+z\nYnScm1RlKk+UstlwcpLLZV1fNE43AWvNhI+zrbUt2C4W0/EU3DhLfa5dCK/v983nw2cxMjJS8Zxe\nLheOLRhL9Jx8n7/fY8cOVTy/yLmwYsUKbNq0qdHDEBE5K8kST2OaSJhyXAt/k1xDchPJTRb9pS8i\nIiIy1Z3zBH4zW2dmPWbWc3rYTkRERGRqm0iY8iwW/jb4t2M+/SjOW6ILYfqwGQAUrFCxz+dGZavk\ncGUzcbgxvX4cwcy68GPutONScXhxcDAsFmzFam8G04sODQ0FPePNBctEA/fHxSFMf8b4Gfrjgj7V\npBMREam5ibyqOp8X/hYRERFpCmf9ZszM8iRPLfydBXDvebTwt4hMYivWPtToIZy3dtx1Y6OHIDLl\nTCRMCTPbgHQB7HHxYS8fDotLLzCbGfWYaucAwm//xWE7+usVo7Couzzj6g15F7aLw6muXcjH46z8\n7cYYme5brXzFmTyLSvudtm90u/65VSuJISIiIhOnjHoRERGRBtJkTERERKSBNBkTERERaaAJ5Yyd\nDZ/T5avHx8yVYihUy3eqwgpR2Qu/kQvPEeSXRSUq4MpZxHlhYV5amF8VV7LIujy40yvwp9vV0rSq\n5X5VK21xWh5asOJA+L/B2S6RJSIiImdOb8ZEREREGkiTMREREZEGqmuYMsMMWltby9s+ZHnaItdB\nqYkwbBaWjIhieuar+kddvsr+aVX93coA0XFFt3B4XLnfV/m3aGobV/IPy1DEZS98SBEVnV5qonIF\n/mLRhyIrrxyQifoqlstQmQsREZGa05sxERERkQbSZExERESkgTQZExEREWmg+pa2YJjzlMvlfFe0\nbzpPzGbDXp9fFpdhoDvu9L40F4q58JzmcsYsmqMWXC5WllH5iJH0GkM2GPQVo/PkXAmJXDYuNeGF\nOVzVSk34rriURpjuFY4l43LfWGVK7vPHlDEmIiJSe3ozJiIiItJAmoyJiIiINFB9w5RWuSp8NhcO\npeDKMmRYrbJ92OdDmtlcONcMQpgIS0uYK4mRQWvQB6ZjKw6HIcO5s+aW252zuoK+weFwhYGjR4+V\n2wODx4K+lpb0Gn4sYym4VQbi6vxhGYwoLOsDo3EJEFXgFxERqRu9GRMRERFpIE3GRERERBpIkzER\nqQmS15PcSnI7ybVV9ruaZJ7ke+s5PhGRZlX30ha+nMXw8HDaxcq5X/lCPjwNzbWj41rSdmtL0AW6\nEhXVykBYMXwsuVx7uX3VG94Q9L3uksvK7e5Z04O+OQvmB9s/ePz75fbzLz4b9L2wfZsbQDRud4+n\nL4eUipdD8tsWrfHk88LiXLNKeX3KJJNKSGYB3A3gOgC7AWwkud7Mtoyy36cAfKP+oxQRaU56MyYi\ntXANgO1m9qKZDQN4AMDqUfb7HQBfBnCgnoMTEWlmmoyJSC0sAbDLbe9OPisjuQTALwD4XB3HJSLS\n9OoapiQZhArb2trK7Xw+DEXmXKkHZsIAmSvAf1rYrsWVs8iEheyRyVS+3WI+PSmjshcXLF9abnd3\ndAZ9rbn0Hn7mp38q6Nv64gvB9rTp08rtj/7u7wR9n/vLz5bbP35qc9DX0hLFWyuoGsLMRisH+DAl\n4pUKOOp+qsAvE/TnAO40s2K1/1dJrgGwBgCWL19ep6GJiDROfXPGRGSy2gNgmdtemnzm9QB4IJmI\nzQXwbpJ5M/tnv5OZrQOwDgB6enqUqigik54mYyJSCxsBrCR5IUqTsJsB/KrfwcwuPNUm+QUAX48n\nYiIiU5EmYyIyYWaWJ3kHgIdRWun+XjPbTPL2pP+ehg5QRKSJ1XUylmEGnR0d5W1fUqG/vz/Y1yzt\ny+Wi5C+MXnqhtO3axShPCu48FvYVXemH7q7wem1udaRMlL/W03NlelxnR9DX1hKe55ffl3657PXX\nvjroGxkeKrf/x9pPBH29vb2oxD/DeBUjX9oiEyXQMfP/s3fnYXJd5Z34v9+q6updLbX2vSVZtrEx\ntrGwgUAwixMTkhiSQCAZMIkZhwwkZIYkaLZMMpP5xfkxWchA4jEMgwkMBBIyOOCEOB7MFjCSjXdj\nS7a17y31vtT2zh91dc95r7qlltSqaknfz/Po6XPrvXXvqVvq7tP3PfWeqFxGZvml+Jpmy2WITMfM\n7gVwb+axKQdhZvbuRvRJROR8oN+0IiIiIk10ysEYyU+SPETyieixXpL3kdyWfF1wbrspIiIicmGa\nSZryUwA+CuDT0WObAdxvZncky55sBvChUx6JcPUR4nILLZly+aXSRHhapixDnLbMVuePT1CrZtJv\n1ajSfOaj9W1RKY3b/sVPuNiGSzek7YMHfC7whT070vb6K/tc7OafeoPbbu+KUrT0/X7Zy0Jl/1Wr\nV7jYk4+HIuaZjCJKtfg4PhVZiEKW91X241UMLOcPSnec6PWepByBiIiInJlT3hkzs28COJp5+BYA\ndyftuwG8eZb7JSIiInJRONMJ/EvNbH/SPgBg6XQ7xgUcC4WZFS8VERERuVic9QR+q3/0btrCjGZ2\nl5ltMrNN8SLhIiIiInLmd8YOklxuZvtJLscMF/01M9SikhVxWYbsWjuFYrQcEqdfriefGU/mou3s\ndDJDdG7zSx6tieZp3XD1lS62eEVP2l67ZomLFTvD8ntrL+/zfcm8pslSOWxk5l91dHWm7cuvuNTF\nnnlqe9quZpcuaonmyNUyc+TK0bXxU8ZQrcXLP/n/Brl8tJ0rQ0RERM6dM70zdg+AW5P2rQC+PDvd\nEREREbm4zKS0xecAfBfAZST3kLwNwB0AbiK5DcAbkm0REREROU2nTFOa2TumCb3+9E9nqEWlGOIq\n+9m0YUtUvf7EKvthO5/PVJaPKutXM7k5V1k+kyY8ejRUuf8v//njLvbTb/3xtP3O2z7gYq2dIYU5\nWfZ5UZeWBDAxEarst7a1+n3LYd+VK1e6WKEQ9q1V/bUotoQPRdD8680xXItaNmcbrU6Qr2VLVoT3\nwuL6GNSazSIiIrNNFfhFREREmkiDMREREZEm0mBMREREpIkaWvjLYKjUSuk246WLan5OU1ymIVuf\nrBzNr2J2iZ5ovaATY1EzMw9taGQ8bY8PTLjY0cEwF6tQbHexXCEcZ3hozPfTT4NDrRbNWcvEKlGs\nd8kiFyu2hzljlUl/nXLFcKBa1c9RK0TLGtWqfm5dLZr/NTky6vtdCudYf8mqtD3Un12IQURERM6W\n7oyJiIiINJEGYyIiIiJN1Ng0pRmqlalTjNk0Zbkc0mgtLX5Ny/h51arP93Ga/QCA+fh8/nmVqF+X\nX3m5i1324hel7VzBp/vyhTCe7Wgvulitmjl/1J+WFn/pOztDavI1r32di/39392Xth969GEXK3ZH\n/aHv2+RAKKVRyPm+tba3pe11Vy5zsSi7iY1Xrknbe7btgYiIiMwuLRYpIiLnTN/mrza7C+etHXe8\nqdldkAZRmlJERESkiTQYExEREWmixqYpLTNXKyqvUKv5pXzizUrFzyeLl0A6cc5YVNoi58eavpyF\nn8/V1R3mUL38R1/iYlddG+aMFYr+ksWH7G73SxxZZpmh+Oy5nI+Vo/4sWuhLW7z4mjCH7eDQDhe7\n5Jr1abu9q8PFHv3eU+F89PPu1l0a5oK1z/PXfn1fX+hXOZT5KLb6Y4iIiMjZ050xERERkSbSYExE\nRESkiRpe2qISlaXPFeNUne8KayEVWS6VXKy1GNKBeWZTkaEdV7yvx0I6jvSxlauWpu3Lr3ixi/XM\nD2nDatWnTIu5KHXns33ILgAQK2dKa8Rp08nSuItceu3GtF3p9asDLFzenbbbWnz5ioULF6bt8dFh\nF1u3YXna7j98yMUO79mdtjvaQurTqpkXKCIiImdNd8ZEREREmkiDMREREZEm0mBMREREpIkaOmes\npaUFy5aGuVn7+/vTdj4zLmQ0F6yaKW1RQphD1trqy0mUy2FeU3bJo3h6WT7vJ3Rd/qJLo376ZYUm\nJsI8rcHBQRdbtHBJ2s5U0sD4xKTbHpsM2+1dXS4Wl+iYzJT5WLw8XLMVlX4XO3gkLFHUmSmt0bMg\nbPcu8LHRkbG0nWe3i5UnRtP248++kLbHx/3rERERkbOnO2MiIiIiTaTBmIiIiEgTaTAmIiIi0kQN\nnTPW3tGGl1wdLe3z9e9GUT9PKpcL87YqFR+Ll0fq6Gj3z2N4Xq3q65MxF2qLFTPLGi1bFmqJLVs6\n38VqtXCcI/1HM8cMyygtmD/PxSbLfq7b0YFQ66u34JcWGi2F+VilTD2vIwfDPLHDu/a7WLE11Bbr\nKHa62LGje9P22JCf7zU4EPrWG83jA4BqMRxz1SUb0nbLA49AREREZpfujImIiIg0kQZjIiIiIk3U\n0DRlpTKJw0efT7dXrwvlHdpbfHmF5364b9rjWLTmUaXiy1cUCiHFli/48hWVSjltt0bL/ABAX9/a\ntD1/nu/LkSNhuaBKLVMiYiKk+yZKi12stc0vT1RoDWPfw0cP+75Fr2lodMDFnt/+bNo+uHevi7W2\nh7ewWl7gYrUoSzrUP+ZikxPhui1d5stszF8c+t3eHtKwbR3+9YjESN4M4CMA8gA+YWZ3ZOK/COBD\nqK/9NQzgV83s0YZ3VERkjtGdMRE5ayTzAD4G4I0ArgDwDpJXZHZ7AcBrzOwqAP8FwF2N7aWIyNx0\nysEYydUkv07yKZJPkvxA8ngvyftIbku+LjjVsUTkgnU9gO1m9ryZlQB8HsAt8Q5m9s9mdizZ/B6A\nVQ3uo4jInDSTO2MVAB80sysAvBzA+5K/eDcDuN/MNgK4P9kWkYvTSgC7o+09yWPTuQ3A308VIHk7\nya0ktx4+fHiqXURELiinnDNmZvsB7E/awySfRv2H7C0Abkx2uxvAA6jPB5lWz/wu/OQtrwzbS0Ip\nhrGjfg7Xn/23v0rbR4/6chL5fFz2wpePiOeTmfn5ZPOj0hM/8zNvdrFLN25M2/v3HHCxsckw16ya\n9/Om2rrC0kGl6rCLLVm6xG2Xq6Gvw2OjLlathH7v2LHdxQaOHQzHqPi5X4VqmBdXGvPlMna/MJS2\ns8tNbdi4Im0X8+ZihfZwnIGB8MuwVvXXWuRMkHwt6oOxV00VN7O7kKQwN23aZFPtIyJyITmtOWMk\n+wBcC+BBAEuTgRoAHACwdJrnpH/ljgyPn0VXRWQO2wtgdbS9KnnMIfkSAJ8AcIuZ9WfjIiIXoxkP\nxkh2AfgbAL9hZkNxzOq3o6b8C9bM7jKzTWa2qau7fapdROT8twXARpLrSBYBvB3APfEOJNcA+BKA\nd5rZs1McQ0TkojSj0hYkW1AfiH3WzL6UPHyQ5HIz209yOYBD0x+hrlatYGgklG24/rWXpO3923zV\n+e6ukA48dsyP8/L50O1Spsp9kSFtt2LVchd7yUtenLaXZqrOHzkcVbk/4Kvc59tC2q6c9+PXwkSU\npiz51GMtV/bbtbgkh+/38FC4LkeO+BsKHfPCIHas6lOR7V0hZXu0f9DFDu0JaVNmqvp3d4bn7Y/S\noACwauOatJ3PxefzpUJEjjOzCsn3A/ga6qUtPmlmT5J8bxK/E8DvAFgI4M9Z/z6tmNmmZvVZRGSu\nOOVgjPWfmv8TwNNm9sdR6B4AtwK4I/n65XPSQxE5L5jZvQDuzTx2Z9R+D4D3NLpfIiJz3UzujP0I\ngHcCeJzk8cUJ/x3qg7AvkLwNwE4Abzs3XRQRERG5cM3k05TfxvT5qdfPbndERERELi4NXQ5pZHQC\n33vwmXT7JTdclbYP7t3j9r3qylB6YXjYLw80GlV3aGvzS/msWBXmgq1c7UtLjI9PpO1vfuM7LpaP\n2pde6ssjtfWEJYFYzCxxVIjmc40ec7Fy1c8ho0Vj2rKfw7V957a0fXDEf8hs8crwOqqti1ysOhnm\nnu3e9YKLDfaHz1mMD424WLE9zF/bcN16F+uMlkCKqoggn9OCDSIiIrNNv11FREREmkiDMREREZEm\namiasmbEeCmk6h7aGirNX73RL1P3Gx98bdpu/x9/7WLf+MYP0nYlU+qhNUojlqK0JADs3RFKRlRL\nvjp/uTSZtrt7fCmNhbVQub/Q6tOUxShNWpn0fTk46cqxgbmQ8+s/6lORgyOltL1kjU+vjpWiNC39\n+JkIKxccG/TnGxwP51iwdL6LXXpNKCty3SuvdDFjKMkxPhauIZWmFBERmXX67SoiIiLSRA29MyYi\nIiLN0bf5q83uwnlrxx1vOqfH150xERERkSZq6J2x1rYi+jb2pdu794Q5TUt629y+69eHRcV/9HXX\nu9gPnnh6ymMAwMBIeEn9xyZdbHI8zMvqbO10MUbLHO3e64+5ds3atL13724Xy7eFOVVtbX7tzZbM\nHKuhsbA80XBlzMVa2xem7fZ2fy3aO8K8sEMH/Lyw/mNhe+mqhS52ycZQHuT6H3mZi61YG0qAtLX7\n+XPHjoa+LehdkLYLhTxERERkdunOmIiIiEgTaTAmIiIi0kQNTVNWqhUcHTmSbs/r7EnbzKT4BiZC\n9fpCt+9m3xWr03ZxgU/pdXV1p+2xoXEXO3zgcNoeHvFV/QuFcJxdB311/GIxHHNy0qf0tj8XUqYT\nZV8SY/1KX8l/wcJQIqNvwyUuti/q2+iY79vSpdFrGi25WOeGcMyrr/PH3LA2pClzLT7F2D8YVgsY\nOOBXDjhyOJx/3ryQ6i2XKxAREZHZpTtjIiIiIk2kwZiIiIhIE2kwJiIiItJEDZ0z1tbeisuu2pBu\nz+sI5SVGM8sT7Tx0NG2XS34u1qXXXpq2V73Iz8uqVcO+owO+tEXrc2G5opGBYRfr6g4lHNq7e1ys\n0BXmkN3w6pe4WPXR0O+hip9rtm6NX+Jp04tflLb3DR1wse6FodRE1fzrnZwI25ddts7FOjrDXLtj\nQ36u2dhkmIc2sNeXxBgZC/2eLJddrLUlzEPraAvvUU7LIYmIiMw6/XYVERERaSINxkRERESaqKFp\nykJLAYsX96bbVg2pslrVl144eCSUW+jqmudixfaw73i1xcX6VvSFjSU+9Xn11SHFODziyznULOxb\nrvi03ZIVoQJ+R7HoYjfMuybEFvoK+D2t/vJWR0KqsFz2adLR0ZBSXbt2g4sVO8I5y2O+cn//SEjn\nVsz3e3B0JG13tS92sZF94XkdHQtcrKUQzsccQxsiIiIy23RnTERERKSJNBgTERERaSINxkRERESa\nqKFzxlryBSzqWZRu79mza9qulCbC3KixIT+/ioVQ6qFW8kv0LOpekrarOV/aYqIQlkdatnqJi+14\n4Zm0bVX/vJFSGLMeOuZj8+aFkhS9C+a72OSwLzWBWniNnOx0ob0796Ttnh5/nLXzQvmOwwMT/nm7\nd6ft1nY/tl60OswTOzI46GIbLrksPC/n5+T94Idb03ahtSt033zpDhERETl7ujMmIiIi0kQajImI\niIg0UUPTlNVKDaMDIf04PhxKLyBTdb6tpTVtT475VOSy1aGExPq1XS7WEaXqJmrtLlbNhTTbxISv\nSN/VGdKGpQmfjhsYCPtWM1X2e3uj85V8CnNweNxtD+w+krY52epiSxZFVf/zJRcbnwypyYlJf46u\nrlCWYkGvL1GxeHE4ZlvRv97axGjo55hPffb2tkVb8fmUphQREZltujMmIiIi0kSnHIyRbCP5fZKP\nknyS5O8lj/eSvI/ktuTrglMdS0RERES8mdwZmwTwOjO7GsA1AG4m+XIAmwHcb2YbAdyfbIuIiIjI\naTjlnDEzMwDHJ3e1JP8MwC0AbkwevxvAAwA+dLJjVcoVHN7fn27nLIwFW1r8ckirloUlgTqLS12s\nwjDfqVbxywMd7Q/L/Axn5pr1rghzqCojfl7WvHlh7tko/PNGR8OcqmKLn4c2PBzKbozVfF+6OnzJ\niJKF+VeDhw+42FWvCKUm+sd9SYz+Y6EsRWd3j4tZJcy7K036OV0jQ9F1KvmlkvbtOpi2y+av/bIV\n3WGD4ZjUgkgiIiKzbkZzxkjmST4C4BCA+8zsQQBLzWx/sssBAEunPYCIiIiITGlGgzEzq5rZNQBW\nAbie5IszcUP9btkJSN5OcivJraMj41PtIiIiInLROq3SFmY2QPLrAG4GcJDkcjPbT3I56nfNpnrO\nXQDuAoDV61ZYZ09IgR09FtJoHW3d7nmlUkiPteV9NysIZSEGR4+5WP/RUD6ic74/ZpXheYWiHzuW\nolIPrUVfHf/ggZAKzOf8+LWjvSVtz2/zz+uZ5z/TMLokpDFLY77UxMBISE1OlHw6cGggpCmvunq5\ni7W0hP4MHu13sUo5HKd/v1/FYHIkxOav9NdpYiy83iP94W0tlXxqV0RERM7eTD5NuZjk/KTdDuAm\nAD8EcA+AW5PdbgXw5XPVSRGZ+0jeTPIZkttJnvCBHtb9WRJ/jORLm9FPEZG5ZiZ3xpYDuJtkHvXB\n2xfM7CskvwvgCyRvA7ATwNvOYT9FZA5Lfj58DPU/1vYA2ELyHjN7KtrtjQA2Jv9uAPAXyVcRkYva\nTD5N+RiAa6d4vB/A689Fp0TkvHM9gO1m9jwAkPw86p+4jgdjtwD4dDLH9Hsk5x+f6tD47oqIzB0N\nXQ5pz479R/7Nu35/J4BFAI6cav+L0Fy/Lmub3QGZs1YC2B1t78GJd72m2mclADcYI3k7gNuTzRGS\nz8xuV+eEOfu9zj9sdg8aas6+D4Dei7nkLN6LGf3ebOhgzMwWAwDJrWa2qZHnPh/ouoj4D/1cqPS9\nPjfofZg7Lvb3QmtTishs2AtgdbS9KnnsdPcREbnoaDAmIrNhC4CNJNeRLAJ4O+qfuI7dA+Bdyacq\nXw5gUPPFREQanKaMXNApiLOg6yLnJTOrkHw/gK8ByAP4pJk9SfK9SfxOAPcC+AkA2wGMAfilZvV3\nDtD3+tyg92HuuKjfC9Y/2CQiIiIizaA0pYiIiEgTaTDWZCQfIPmeaWJrSI4kBTVPuq+IiIicnxo6\nGDvVcinN0uhBDsnVJL9O8ikAL0NSPJdkL8n7SG4jeR+AYTPrMrNqo/omIrOL5O+S/M1m9+NCR7KP\n5BPN7ofMDpI3knxls/vRKA0bjEXLpbwRwBUA3kHyikadf46pAPigmV0B4GEAr02uxWYA95vZRgD3\nJ9siInIOkWzWh9lkejcC0GDsHEiXSzGzEoDjy6XMGpKbST5HcpjkUyTfkjz+uyQ/E+3XR9JIFkj+\nVwCvBvDRJCX40WSfV5LcQnIw+frK6PkPkPx9kv+cPOfvSC4k+VmSQ8n+fdH+7lgA1pnZw0m4CmAI\nwJcAfBDAjSR7AdwN4OeO93Oa1/vLJJ8meYzk10iqQr7IHEDy35N8luS3AVyWPHYNye8li6T/LckF\nyeMvSx57hOSHdXfnrORJfpzkkyT/kWT7Sa77AyT/lORWAB8g+VaST5B8lOQ3k33yyXuyJXn+rzT1\n1V0ASL4ruZaPkvxLkj9F8kGSPyD5TySXJr8/3wvgXyffF69ubq/PvUYOxqZbCmU2PYf6wKoHwO8B\n+AzJ5Sd7gpn9ewDfAvD+JCX4/mQw9FUAfwZgIYA/BvBVkgujp74dwDuT17ABwHcB/C8AvQCeBvCf\ngHrq8RTHagOwHsCtAIYBjCb7HgCweLp+k7wFwL8D8DPJft8C8LmTXx4ROddIXof6z4drUC/l8bIk\n9GkAHzKzlwB4HMnPCNR/bvyKmV2D+h9ncuY2AviYmV0JYADAz2L66w4ARTPbZGZ/BOB3APy4mV0N\n4KeT+G2o18N7Gerv478kua5Br+WCQ/JKAP8BwOuS6/wBAN8G8HIzuxb1mzS/bWY7ANwJ4E/M7Boz\n+1az+twoF9QEfjP7opntM7Oamf0VgG2o35E7XW8CsM3M/tLMKmb2OQA/BPBT0T7/y8yeM7NBAH8P\n4Dkz+yczqwD4IsLi6tMei2QXgCsB3GNmDyb7/0cAb0P9vTlZ3ZH3AvgDM3s6Oef/B+Aa3R0TabpX\nA/hbMxszsyHUi912AphvZt9I9rkbwI+SnA+g28y+mzz+vxvf3QvKC2b2SNJ+CPU/lE+47tH+5m23\nwgAAIABJREFUfxW1vwPgUyT/Jeq18gDgx1AvVPwIgAdR/4N647nq/EXgdQC+aGZHAMDMjqK+EsfX\nSD4O4LdQ/5140WnkYOycL4WS3P58hOQAyQEAL0Z98dHTtQLAzsxjO+Hv5B2M2uNTbHed4lirAfxN\n8rz7o2NOAGhBfV7dyRZNXQvgI9FrPQqAmP27jSIi54vJqF0FMP8U+48eb5jZe1G/a7MawENJ9oIA\nfi25O3ONma0zs3+c7U5f5P47gI+a2VUAfgX1bNFFp5GDsZksl3LGkjtCHwfwfgALzWw+gCdQ/2Ya\nBdAR7b4s8/TsHah9OHGl9TU4s8HjdMe6GfV05h6EQeo9AN4HoAzgJwHcd5Lj7kY9tTE/+tduZv98\nBn0UkdnzTQBvTuYrdaN+R30UwLFo7ss7AXzDzAYADJO8IXn87Y3v7gVtEFNc96l2JLnBzB40s98B\ncBj1n8tfA/CrJFuSfS4l2dmAfl+o/i+Atx6fppNM4+lB+N16a7TvMIDuxnaveRr2CZLplkuZxVN0\noj6oOgwAJH8J9TtjAPAIgA+RXIP6N+e/zTz3IOrzto67F8B/J/kLAL6A+ryDKwB85Qz6NdWxrkL9\nzll3ct5XknwBwEdQv7U+gXq5iw+i/pfCVO4E8F9IPpIsO9MD4MfM7Itn0EcRmSVm9jDJvwLwKIBD\nqP8hCtR/0dxJsgPA8wjLQd0G4OMka6gPFAYb3OUL3XTXPevDJDei/gf8/ai/f48B6APwMEmi/vvl\nzee8xxeo5HfVfwXwDZJVAD8A8LsAvkjyGOqDteNz8v4OwF8n86N/7UKfN3ZBLYeUvMm/CqCG+qTN\n6wD8pZl9guTHAPwi6qm/P0R9HayWZJD4CtTnEixO9v91kq9CfXB0Cepr6X3AzL6dnOcBAJ8xs08k\n278PYJWZvTvZfgOAO83skmT7VMf6LuqDr8tR/2H8S2Z2JPlEyQtRP7PnfSeA30b9ztsggPvM7Jdn\n85qKyLlFssvMRpL2ZgDLzewDTe6WiDTQBTUYExE535D8edTv1hdQn0/6bjM73NxeiUgjaTAmIiIi\n0kQXVGkLERERkfONBmMiIiIiTXRWgzHO0YW/RURERM4XZzxnjPWFv58FcBPqtbK2AHiHmT013XM6\nOzttwYLedLujI5T+amlpcfu2t09f961cKqXtkWH/KfCJ8bG0XalWXKxQyIcNMnO+0Jeurh7/vKhv\nIyNDLnb4SKj1Wq6UXaxSqbntk13q+H1gpm8x5njS7Vg+HyqX5OjH3a2FYojlfSyXC9vdnaHMy4ED\nBzEwODj9CUVm2aJFi6yvr6/Z3RAROSMPPfTQETObdmnD486mzli68DcAkDy+8Pe0g7EFC3rx/l/7\n1+n2S697adpetmyp2/fFV1yWtnOZ+3f79uxK29/+v/e62LNP/iBtHx3wBex7F4VizLkW/9KvuPLa\ntP3KV/+kiy1ZtiKc7zu++PJdn/hvaXv/kYMudvjouNuuVsI4plrzS9BVy2Eg11LwA1HLh4Fasb3o\nYsXWMFA0+tFe97wFabur6GvnrV2yJm3P6/LH7OgINQ1f9YrXp+3bb/9XEGmkvr4+bN26tdndEBE5\nIySzK/BM6WzSlDNa+Jvk7SS3ktw6OjqaDYuIiIhc1M75BH4zu8vMNpnZps5OrSIhIiIiEjubNOVp\nL/xdrpZx6Nj+dPu5ndvS9ljJ3zVrjdNvVZ9+GxkaSNuDIz7dd2QozBMbHffTm8rHwnwyP5sLqBXC\nncRFK17wxzwW5ol9//sPudjY8ETazudaXayr28+Dq0avo1SadLHSZNh3bMT3rqU1vE0drT7dWKmF\n+XOlzJy1Y/3hOg1lVlgZHw6vqaerw8Ve/cofTds1C8e3E5bwFBERkbN1NnfGzunC3yIiIiIXgzO+\nM9aAhb9FRM5I3+avNrsL560dd7yp2V0QueicTZoSZnYvgHtPuWNiZGQE33nw2+n2kz98Om339W1w\n+z72ePhQZk9nl4stXxI+Jbp09XoXW7pyXdpm1acwJ8shTTmZSenlo08w7tpzwMV27NqXtottC1xs\n2eorw357fHqzLefPUSqFlGYu51OYHZ3hrVjQ629Y5gthu6WQqSzB9rQ5POZLeVQr4fV3tvsUajlK\nOY6WfT93Hwyp5Io9nLbHorIhIiIiMjtUgV9ERESkiTQYExEREWkiDcZEREREmuis5oydrkqlgoOH\nD6fbY+NhjlPVfFe2Px9KTaxcssjFFswL5R16en3l/je87qa0Pb/VjzWZCyUjSpkK+KVyiPUPHHOx\nkfEwp2rhyl4Xu3ZJqO6xdM/zLvbd79zvtvcfCK+d8HO/3JJE9H3raAv12a68dpOLrVwRVRcp+rl1\n+WisXav4Y46Vw/y1yWguGwAgKsGx+8Bw2i6V/TFERETk7OnOmIjMCpI3k3yG5HaSm6fZ50aSj5B8\nkuQ3Gt1HEZG5qKF3xkTkwkQyD+BjAG5CfWm0LSTvMbOnon3mA/hzADeb2S6SS5rTWxGRuaWhgzEz\nQ6USUmADx6IU2IQv3h+nzlp9FQhs3xZKYtToSzYsjRb1vnLDMhdbvTr87OdEycVgIRW5YL5P96El\npOcOHPOLfw+VwyXceOUNmfOtdtt/+zefTdvPPLvNxVgLacv2dl8R/8dv+um0/c6ff5eLxdU7jmVK\nT3QUw3F++Mx2F3t+XyhfgZKvrG/V0JfepaHkR0vrX0NkGtcD2G5mzwMAyc8DuAXAU9E+vwDgS2a2\nCwDM7FDDeykiMgcpTSkis2ElgN3R9p7ksdilABaQfIDkQyTfhSmQvJ3kVpJbD0dzTEVELlQajIlI\noxQAXAfgTQB+HMB/JHlpdiczu8vMNpnZpsWLF2fDIiIXHM0ZE5HZsBdAnJdflTwW2wOg38xGAYyS\n/CaAqwE825guiojMTQ0ejBFWCzfjLCq3UCv4uVjz2sMyP5Pjky42Wgpzo9oKNRcbOhbKUvzzo34O\n1drBgbS9anGPi7XGNwnpy07Ma8+HULRsEgBMHhpK2y/s98sorV+1wm2/5S2/kLa/+MXPudiefWHJ\npVde/0oXW9izMG0//PAWF1u8KJT9WLLKz1GLq2ds37nLhb65JSwjOpn5b1Ashnl4vfPmpe2x7Dw7\nkWALgI0k16E+CHs76nPEYl8G8FGSBQBFADcA+JOG9lJEZA7SnTEROWtmViH5fgBfA5AH8Ekze5Lk\ne5P4nWb2NMl/APAYgBqAT5jZE83rtYjI3KDBmIjMCjO7F8C9mcfuzGx/GMCHG9kvEZG5rrGDMRK5\nQjhlxUIF/tZuX6KivRj2O3Jkn4tN1ELaslqpuNjw6EjaHqv62K6DYQrLpet8CnHN0lD2ojXvU595\nhu18oehiK7pCP8dGh11s+/M+Nbh+TUgjvvGnftbF9u4MpScWLfCTlv/qC6GkxKLe+f6Y69el7cUr\nfZpyTd8labu905frqEYvsVTy6dxCNaQju6Pz5X32VkRERGaBPk0pIiIi0kQajImIiIg0kQZjIiIi\nIk3U0DljZA65Qpi71FIMJSOsrdPtOzwe5n5ZxY8ZFy7tS9tDgyMu1j8UllHqWdDrYocGjqbtx7b5\nEkhD42FJoL7lfs5WZ0s4f6Hsyzvkoyu4ose/hp2HfbmOF/aG869fucbFVi4N5SsOHDjqYje+7sfS\n9t7dO/059oRyGmyb52Ld80Ipj7ainzPWkg+vtyXnJ4Plo5oY5bFwPa3ml00SERGRs6c7YyIiIiJN\npMGYiIiISBM1uM5YHnl0p1udPSEdmGv3pS0qtcG03ZcpQ3HFVVel7QP7fLqxpRiOny/6avnHhkOK\nsa3iS1Q883xI9x0dnHCxRV3hOG35qovValGNCPNpvGF/GIxWw+oA5eFDLjYxciRtl0r+OK997Y1p\nu721xcUOHdiftouZ1zsYrUZQbM27WN/ykBbd2z/qYrl8eC+GJ6IyIjVf8kNERETOnu6MiYiIiDSR\nBmMiIiIiTaTBmIiIiEgTNXTOWK1axvDg4fBAVG5hQc9Ct+/CNUvT9ujkpIt9/VvfT9vdLX5+Vdv6\nUJZhed86FyuXw75dnR0u1t3RHrqV85eloy3MxVrY3e5i+byfixXrHff9rjEcdyjzmo6Ww7i4UvHz\n0r6/9ZG0vb5vrYtdesnGtL24x5ev2P18WGJp/gK/jFLf2lBaY8fBARdr7QglMgq50K9HvvFFiIiI\nyOzSnTERERGRJjrlYIzkJ0keIvlE9FgvyftIbku+Lji33RQRERG5MM0kTfkpAB8F8Onosc0A7jez\nO0huTrY/dKoDWa2K8nioLj9wNJy+lvdlE4rFkEZr7/ApxdZaSBuOjPS72PadL6Tty196g4u99W1v\nDccv+FIavVGKb8Vif75F80IZjPaCH78ySuNVMpUfxoeG3fbI8FDaPphZOeAyuyIcp+Sr/A8PhTTi\n2JBPKe7ZuSttTy5c4mI9C5el7SVLfJpy0eKwOsE1NV+BvxS9kPHJ0JfODn/NRERE5Oyd8s6YmX0T\nwNHMw7cAuDtp3w3gzbPcLxEREZGLwplO4F9qZserjR4AsHS6HUneDuB2AGBu+snuIiIiIhejs57A\nb2YGYNoVpM3sLjPbZGabcjl9XkBEREQkdqZ3xg6SXG5m+0kuB3DolM8AYGawSjndbovmiVXH/Fyo\nfTtCeYclK1e72KrVK9N2vrDSxQYHwjJKz+/Y7WIv3RTmUC1cttjFylE5iUNDvuzEaCnEcjVfdoIW\ntsuVCjw/F8uiuVks+nlpva3RvLQW/7zywp60PT425mKVajj/wX5/DZ/fF17H0qM+dslEWKtp/ZpV\nLtZeCP8t8tHrzfluiYiIyCw401tV9wC4NWnfCuDLs9MdERERkYvLTEpbfA7AdwFcRnIPydsA3AHg\nJpLbALwh2RYRERGR03TKNKWZvWOa0OtP92S5XA4dHZ3pdluxJW13dPm03WQ5pMf69+5xsUppPG0v\nWbXCxXqXhvIOtUKLiz297dm0PVH2qcgVy8NxJiZ9Pm5iLKQfC/lM6jEqA1HKpPEmqz5tWSmHFG12\nkl17McQWdbW5WGdbqIjflvcrAFTKId3YhWMulmsNxynX/Icnnt8RPiBrVR9buypcw3md4Xx5zfkT\nERGZdfrtKiIiItJEGoyJiIiINJEGYyIyK0jeTPIZktuTlTmm2+9lJCskf66R/RMRmavOtLTFmZ2s\nUEBvb1iGp38wLBc0Mjru9u1dEEpPtLX4MWNpJJSvOLjHz8uqRvO0Oju7XWxJNC9tbHTQxQaPhnlT\nS3qXu1g83WpycsLFcgx9y86p6orKVQBAqRzmsA2OjLrY0eFw3OERP5+toy2aW1f0b1l8aZib52Jt\n7VHfMvPnCgxz8nbtP+Bi4xPhvVi/NpS9qNYy6z2JJEjmAXwMwE0A9gDYQvIeM3tqiv3+EMA/Nr6X\nIiJzk+6MichsuB7AdjN73sxKAD6P+rJpWb8G4G8ww9qEIiIXAw3GRGQ2rAQQV1nekzyWIrkSwFsA\n/MXJDkTydpJbSW49fPjwrHdURGSuaWiaEgByURn3XFTgYWjIl2WwUilt98xf4GLt+bDNSV+WYaS/\nP23vp4915MP5lly6wcXmdYYyEO2tZRfr7ArlOEbHfAX+ycmwby6Txhsd9q+ppRDSlr3tvm9DFp47\nMuHPMT4Z0oaHM+dgVOU/V/OpyHwxxDo7Sy62bGE4f1d7p4sNRmnSHfsOpu3JUnaFAZHT8qcAPmRm\nNXL65RzM7C4AdwHApk2bpl1qTUTkQtHwwZiIXJD2AojXLVuVPBbbBODzyUBsEYCfIFkxs//TmC6K\niMxNGoyJyGzYAmAjyXWoD8LeDuAX4h3MbN3xNslPAfiKBmIiIhqMicgsMLMKyfcD+BqAPIBPmtmT\nJN+bxO9sagdFROawhg7GSCCfD3OVWvLh8wOs+flI5XIo/TAwkIlVw5yq9k5fzqFgYS7KGPz8qkcP\nhxIOw0f8xODXvOY1afuytb60RU9PKJExWfbnO9QflhWqTvqSFK15v++x4bHwGip+XlixJcz3avch\njE2GaTPjZT+FZjxaNiq+LgCA0bDdNeJjbVHf2uf7p8VLUe07MjRtn0ViZnYvgHszj005CDOzdzei\nTyIi5wN9mlJERESkiTQYExEREWmihqYpK5UK+o8cSbfHxkLarlryFfhbiiE1WDmhZESonp99AZNR\nanJs0NeVPHAwpCaffuQhF5sYDOnGhfPe4WLrN4YyGF1dvgzE4vmhn7v3+fIRew6PuO2RUkjzjZUy\nn9jn9NXtB4fDtZmo+OeVorRsJZOWLUYrArTRl704eDSkVMtj/tofPRqu29LloVSUKvCLiIjMPt0Z\nExEREWkiDcZEREREmkiDMREREZEmauicsWq1iqHBsERQKVryyDJlGSbHwzym9u5WFzML+44MD7hY\nPhfNGRsbdbHSaNi3t7fXxR75/rfTdqGt6GIvfcXL03bfGrfcHpbO70nbo2N+PteO3Qfddv9gKBOR\nL7a7WK4YlmMq1/xxqhZt5/3cr7bW8LyuvF9ipoXhOrHkr8XR4bC97cAeF3visUfS9o/9+M1pezJT\nukNERETOnu6MiYiIiDSRBmMiIiIiTaTBmIiIiEgTNXg5JKKlGOZ/MReWRqpm5oxVo+WRSqODLtbZ\nFa3fk5lDNTQSapdVMrXL2trCubP1wopRv5596ikXi2t7PbNkiYutXrk6bW9Ys9bFNizvdtvL5oXX\nW6mUXaxrflie6IV9vj7a0WNhrtuiJctcrFwOsR1PPuZiB/eGuWADx4642IplS9P20mV++afu7q60\nXatEtdMsUxtNREREzprujImIiIg0kQZjIiIiIk3U0DRlLpdDsT0q6RCXSqj6pXYKtWiZn5JP6Y1E\nyyF1dPsSFYVCeEmkT6sxWh6od+ECFyuXQ1q0UvIlHApRem7f3gMutn9/SBOOD/gyGy950Tq3vXZp\nVAZjZMjFNl4SpQrH+l2sdTy8/pU9vnxFqRJe77OZdO4Tjz8ajtHqy4O0FEJ6t69vvYtdd+01aXvH\nc8+Gc01OQERERGaX7oyJiIiINNEpB2MkV5P8OsmnSD5J8gPJ470k7yO5Lfm64FTHEhERERFvJnfG\nKgA+aGZXAHg5gPeRvALAZgD3m9lGAPcn2yIiIiJyGk45Z8zM9gPYn7SHST4NYCWAWwDcmOx2N4AH\nAHzoZMcic2htDXPGDGH+k02WMjuHeVotRT9PqlwOsbFhP0+qo7MjOr6fh7YgWrpo48YNLjY8NJK2\nd+3c548ZnX/NOl++ov9ImPt1+LBf/mjfIl/aYngivMYj/UddrJIP12XXnv0uNhotDdUR9RMAatF4\nevUlV7jYy3PhmDRfOqSjNcwZmyz5a7939660/chDW9J2dnkpEREROXunNYGfZB+AawE8CGBpMlAD\ngAMAlk7znNsB3A4A+ULLVLuIiIiIXLRmPIGfZBeAvwHwG2bmPgpoZgZgyoqgZnaXmW0ys035fH6q\nXUREREQuWjO6M0ayBfWB2GfN7EvJwwdJLjez/SSXAzg0/RGS4+RyKLZFqbOoAj/oB2qVchgnWs7H\nigwpt2rZl70ojQ2HjYJPb3Z3h7ThggW+JMbkRChnMTnpU4EPfucbaXvtJZe42CUbLk3bPQv9zcHR\nqu/33t2H0/axoTEXe6H/h2m7UvXj2lw+VMQ/djBTXiLaNVfzadl588LrzVX88/K1kJqcHPevd3hw\nIIqFftYyxxcREZGzN5NPUxLA/wTwtJn9cRS6B8CtSftWAF+e/e6JiIiIXNhmcmfsRwC8E8DjJB9J\nHvt3AO4A8AWStwHYCeBt56aLIiIiIheumXya8tsAOE349bPbHRE5X5G8GcBHAOQBfMLM7sjEfxH1\nT1wTwDCAXzWzR084kIjIRaaxyyGRaG0Jp8zFE54y85FKuWg5pMy8sCrCfCdmyldUKmFZo8kJX7Kh\nFG0fPeJLS1QroS+FzKc++w+H5Yl+sOUhF3vsB1vT9uq1fS62bsNlbjtacQljE77URNUYtTGt7Lyt\nSjm8psFjh11sfldb2l6/ermLVUuhXEb/mL++Y6VoaahMSQyRqZDMA/gYgJsA7AGwheQ9ZvZUtNsL\nAF5jZsdIvhHAXQBuaHxvRUTmFi2HJCKz4XoA283seTMrAfg86rUIU2b2z2Z2LNn8HoBVDe6jiMic\npMGYiMyGlQB2R9t7ksemcxuAv58qQPJ2kltJbj18+PBUu4iIXFAamqYkgGIhnDJPTtkGgFwpjBPH\nLZO3s5CqM/pYNUrj5XN+rBmfoRKl4gCgLVoZoLWtzcUsSiF2tHe6WLkWSmLsfO45F9u1zW/T9cC/\nXkZp2ezKAbWaxTtO+zzWfLqxddWatH2g1b/Vo5Mh/dizcKGLdbaEWC2+1tn3QeQMkHwt6oOxV00V\nN7O7UE9hYtOmTfpPJyIXvIYOxkTkgrUXwOpoe1XymEPyJQA+AeCNZtafjYuIXIyUphSR2bAFwEaS\n60gWAbwd9VqEKZJrAHwJwDvN7Nkm9FFEZE7SnTEROWtmViH5fgBfQ720xSfN7EmS703idwL4HQAL\nAfx5vZY0Kma2qVl9lsbo2/zVZnfhvLXjjjc1uwvSII0tbZHLob09zM0qRyUrcpn5XbVoTlWlmlmG\np1aNYn6eVLz+ZbYMxMhIWPYnl/fna2trTdulUqYkxmS0lBAzi53ni2mz2OJjlunbZFROopaZ35XL\nhb62Fv1xWts70vZ4ptLEWLSMU0tmds3R4XC+zmWtLrb6so1puzrplhrFticeTtuV6FpozpicjJnd\nC+DezGN3Ru33AHhPo/slIjLXKU0pIiIi0kQajImIiIg0UWNLW5BoyaTyphOnKZEpezHBKKXXknex\n8bGxtJ1Nq41FsY6ODheLS1t0d3e5WKkS0pS5qk/31SqhL7VMOtVqPt25oHd+2u5d2ONikxOjaXvp\nksUuVmgLfd22039ALdceXmPfel/xf+OG9Wm7Ne+v4fPbQmH0Hz71uIvFqxq0FgvR4yIiIjLbdGdM\nREREpIk0GBMRERFpIg3GRERERJqo4XXGGM3/iuePMTMvLJ59Van6pYvi57W2+6WLqpWwb6WSKS0x\nGcpAPPzwwy62ZvXatL1z5y4XK5fDnDEW/Pg1XwjzuZj389cs85rGo/OPjvn5ZGbhrRie9M/riV7j\n5Zdf5WJdPQvSdqni58g9t+2ZtL1725MuNnxkf+h3wfe7o2te1DEfExERkdmlO2MiIiIiTaQK/CIi\nIhcBrYZw5s71aggNL21RKIRTxhXys9Xy40IKOWZSg7mQOpuYmHCxuJpFoaXoYnGV+12ZVOTePfvS\n9sjYiItVKiGlWB7158sX4nIZvlxFV/c8t02GfpfLvpR+S2tId45M+GtRGwhlL2o1Xy1/8Kmn0/bR\n/TtcbGx0OJw7s8JBPh/eh2zl/lqU3s21+lIeIiIiMruUphQRERFpIg3GRERERJpIgzERERGRJmro\nnDEDUK1Wp4zlMnOaWvLRdovvJhHmgllmCaKWYpgAxcxcMyuEWDXzvJHRMBerVPbzwhDPZ8vMbSuX\nwpyxkclMKY2RQbedz4eSHLlcpgxGNNmtUvOTuCw6Z7ZcRzUq+9GaqULREs/PQ4aF55n5Uhp24t4i\nIiJyjujOmIiIiEgTaTAmIiIi0kSNrTNm5kpYxGUu4jQd4NOW2er8cWmLlqIvX+GOkjlmfI64zAUA\ntEYlHGrmU4GsxiU4/Pg1lwvHtJpPwZYm/MoBwDimk3397vyMy3z4a1GM07mZtGzN4pStf73xnplD\nIi4rcrJ+iYiIyNnTnTERERGRJjrlYIxkG8nvk3yU5JMkfy95vJfkfSS3JV8XnOpYIiIiIuLN5M7Y\nJIDXmdnVAK4BcDPJlwPYDOB+M9sI4P5kW0REREROwynnjFl90tDx9YFakn8G4BYANyaP3w3gAQAf\nOunBSOTzYb5XXOZiupIXSR+mjcXzzoB4thOQz8yFqlTCHK7s8ktmYdvg53qVSuFA2X6euIzTzGJZ\n8WvMvlpGr+pk87uy5UFONu/uZPPQCoV81C5Ez5my6yIiInIWZjRnjGSe5CMADgG4z8weBLDUzPYn\nuxwAsPQc9VFERETkgjWjwZiZVc3sGgCrAFxP8sWZuOHEGzoAAJK3k9xKcmu5VJpqFxEREZGL1mmV\ntjCzAZJfB3AzgIMkl5vZfpLLUb9rNtVz7gJwFwDMm9djcZrvZGm8OMUWpzaTY6btbNqQ0fNOSGHy\nZOnGsN1SaJk2lk33xa/hZOU5svueqZOlG7PXKY6d7Hk29TgaAFCMSodkVzQQERGRszeTT1MuJjk/\nabcDuAnADwHcA+DWZLdbAXz5XHVSROY+kjeTfIbkdpInfKCHdX+WxB8j+dJm9FNEZK6ZyZ2x5QDu\nJplHffD2BTP7CsnvAvgCydsA7ATwtnPYTxGZw5KfDx9D/Y+1PQC2kLzHzJ6KdnsjgI3JvxsA/EXy\nVUTkojaTT1M+BuDaKR7vB/D6c9EpETnvXA9gu5k9DwAkP4/6J67jwdgtAD6dzDH9Hsn5x6c6NL67\nIiJzR0OXQxoeHjry9X/6+50AFgE40shznyfmxHXpx8G0vQvb49DahndGzhcrAeyOtvfgxLteU+2z\nEoAbjJG8HcDtyeYIyWdmt6tzwpz4Xp8K/7DZPWioOfs+AHov5pKzeC9m9HuzoYMxM1sMACS3mtmm\nRp77fKDrIuI/9HOh0vf63KD3Ye642N8LfTxORGbDXgCro+1VyWOnu4+IyEVHgzERmQ1bAGwkuY5k\nEcDbUf/EdeweAO9KPlX5cgCDmi8mItLgNGXkgk5BnAVdFzkvmVmF5PsBfA1AHsAnzexJku9N4ncC\nuBfATwDYDmAMwC81q79zgL7X5wa9D3PHRf1e8GTrPoqIiIjIuaU0pYiIiEgTaTCWIPmLJP/xLJ7/\nbpLfns0+iYiIyIWvoYOxUy2X0kxm9lkz+7FGnIvkapJfJ/kUySdJfiB5vJfkfSS3JV97221/AAAg\nAElEQVQXNKI/InJ6SL6Z5BXn+Bx9JJ+YJvaJ4+cnuYPkonPZl/NFUkj4X83SsW4k+ZXZOJZ40/2f\nJfnTc21s0CgNG4xFy6W8EcAVAN5xrn+YzRaSs/1BhwqAD5rZFQBeDuB9ybXYDOB+M9sI4P5kW0Tm\nnjej/nOsKczsPZmlpqRuPoATBmPn4Ge4nANmdo+Z3dHsfjRDI++MpculmFkJwPHlUhqK5GaSz5Ec\nTu5MvSV53KUZSRrJ95HcBmBb9Nivk3ye5BGSHyY55TUk+RGSu0kOkXyI5Kuj8K8A2Ezy0wD2AVgG\n4EbUr8fdJFcAeDWAD5J8geSvn4NLISIRkv8n+V59MlkFACRHovjPkfwUyVcC+GkAHyb5CMkNJK8h\n+b1kAfS/PX5Xm+QDJP+E5FaST5N8GckvJXe/fz869r8h+UTy7zeibhVIfjZ57l+T7IiOe0KBTJL/\nguT3k379j+SP4IvJHQA2JK9/C8lvkbwHwFPZO40kf5Pk7ybtS0j+E8lHST5MckN80OR9+0H2cTk1\nkp0kv5pc2ydI/nwS+rXkWj9O8vJk33eT/GjS/hTJO5PvnWdJ/mTTXkQDNHIwNt1SKI32HOoDnR4A\nvwfgMySXT7Pvm1Ff0iX+C/gtADYBeCnqg6dfnua5WwBcA6AXwP8G8EWSbVH8p1EfkF4NwFD/mP9S\nAAcB/B2ABwGMoL7+52+Q/PHTepUicrp+2cyuQ/37+9dJLpxqJzP7Z9Rrpv2WmV1jZs8B+DSAD5nZ\nSwA8DuA/RU8pJZXF7wTwZQDvA/BiAO8muZDkdah//9+A+p3yf0ny+HrAlwH4czN7EYAhTHHX5ziS\nLwLw8wB+xMyuAVAF8ItnciHOY5sBPJe8/t9C/ef0B8zs0lM877MAPmZmVwN4JaIlupLB950Abkne\nazk9NwPYZ2ZXm9mLAfxD8vgRM3spgL8A8JvTPLcP9Rs5bwJwZ+Z36AXlopvAb2ZfNLN9ZlYzs79C\n/a7X9dPs/gdmdtTMxqPH/jB5bBeAPwXwjmnO8xkz6zezipn9EYBW1H+wHvdtAN8E8EUA/xH1H84A\n8DIAi83sP9cPY88D+DjqRTRF5Nz5dZKPAvge6isFbJzJk0j2AJhvZt9IHrobwI9Guxwvfvs4gCfN\nbL+ZTQJ4PjnPqwD8rZmNmtkIgC+h/gcjAOw2s+8k7c8k+07n9QCuA7CF5CPJ9vqZvIYL2PfN7IWT\n7UCyG8BKM/tbADCzCTMbS8IvQr3+1U8lP/Pl9D0O4CaSf0jy1WY2mDz+peTrQ6gPuqbyheR39TbU\nv18uP7ddbZ5G5tHnxFIoJN8F4N8gvPldqC9QWp1i992neGwngBXTnOc3AdyWxA3AvOQ8xx0E8Deo\n/0V2D4CPANiF+p2yFSQHAXSRHEC9iOa3Tv3qRORMkLwRwBsAvMLMxkg+AKAN9e/d4870r/LJ5Gst\nah/fPtXP4GwhyJMVhiSAu83s355e9y5oo1G7An8DYibv5/5kv2tRn1Iip8nMniX5UtQLPv8+yfuT\n0PHvhSqm/z44nf//57VG3hmbyXIp5xTJtajfZXo/gIVmNh/AE6j/EJvKVG98PKBcgym+QZP5Yb8N\n4G0AFiTnGcyc5xUAnjazP44e+zvUU5svAPgDAH9kZvPNrNvMfmIGL1FEzkwPgGPJQOxy1NOFAHCQ\n5IuSuaFvifYfBtANAMlf+seieaHvBPANzNy3ALyZZAfJzuQ8x//4WkPyFUn7F1C/oz6d+wH8HMkl\nQPrp7LWn0Y8LQfq+TOEggCVJargVwE8CgJkNA9hD8s0AQLL1+Nw8AAOop8j+IBmwy2lK5kCPmdln\nAHwY9dTxTL2VZC6Zq7cewDPnoo9zQcPujE23XEqjzp/oRH2AdRgASP4SQnpwpn6L5IOo31H7AIA/\nnmKfbtT/CjuM+gTczajfGTtuDYB1AF6XpBOKyeP/P4DPoT7guw3Aa5IJuC8C0G5mW06zryIyM/8A\n4L0kn0b9B/73ksc3A/gK6t/LW1H/vgfq8z0/nny45ucA3Ir6nJYO1NMpM17qycweJvkpAN9PHvqE\nmf2AZF/Sl/eR/CSAp1CfXzPdcZ4i+R8A/GMyeCyjPj9t50z7cr4zs36S30km6o+jPgA7HiuT/M+o\nX+e9AH4YPfWdAP5HEi8DeGv0vIPJ5PG/J/nLZvZgI17LBeQq1D/sUkP92v4qgL+e4XN3of5+zQPw\nXjObODddbL6Lbjkkkv8V9f8MNdQn3V4H4C9RHzy9x8xelexnADaa2fbouYb6AOw3UP9L+lMAftvM\nqiTfffz5yQDq46j/kB4F8CeoT7x9j5n9U/IJnkvM7F8kx+1D/W5YSzJoXQHgjwC8FvW5Zs8A+A9m\n9k/n6LKIiIjMGckfKF8xs5kO3M5rF91g7GxMNUATERGR2aXBmExLgzERERGZbRddaQsRERGRuUR3\nxkRERESa6KzujHEOL/wtIiIicj444ztjyScGnwVwE+pLG20B8I6TLV7LfLuhJS4Bcy7uynHKZkOc\n1suZS3ckT9KXOFQZgVUnGn1V5SK2aNEi6+vra3Y3RETOyEMPPXTEzBafar+zqTOWLvwNACSPL/w9\n7WAMLd3I9f1s2I4GgqcqKz2d7PPidbtzDVgj153fqplYpnc2fWymrzE7eI5vbfIkxYpPHHOHB076\nvFpo1/b83Ul6KTL7+vr6sHXr1mZ3Q0TkjJCcUZ2/s0lTzmjhb5K3J6uub0V1PBsWERERuaid809T\nmtldZrbJzDYh336uTyciIiJyXjmbNOXpL/zNHNjSkW4aamn7TEeFJ6b3ctO0ZwszW3GqNbvW+PTJ\n12y/ZzoRi9l8o9u2TMim2S97iJrfjo+Ti9rUdDEREZHZdjajlaYv/C0iIiJyvjvjO2NzZOFvEZET\n9G3+arO7cN7accebmt0FkYvO2aQpYWb3Arh3xk/I5cC2rnQzTvHlOP1NuhM+lXjSTsWptMam1Yhs\nus87adowSgGeLIV5QimSOMWYSTeiFm9njxr3JZNezR4nPYQWbBAREZlt+u0qIiIi0kQajImIiIg0\nkQZjIiIiIk10VnPGThdzebR0hOWQ4jINzPlxoZsnddISEX4ulK+If6YFJM4Qp5lrlbCTlqHAtLHp\njwFYbfo5Y3H1/CmOFFrViovUpimDUc2d+xUNRERELja6MyYiIiLSRBqMiYiIiDRRQ9OUuVwexa75\n0SMhrZZNN9ItZI2TxHxqLhcfM/PEKopR249DLUox5k5I08UHyo5foxgzC4Vn05YnzRqGfbMV8U92\njFq0by3zvFptZtX5Wc08z/UlWig8p7G7TI/kzQA+gnrdwU+Y2R1T7HMjgD8F0ALgiJm9pqGdFBGZ\ngxo6GBORCxPJPICPAbgJwB4AW0jeY2ZPRfvMB/DnAG42s10klzSntyIic4tudYjIbLgewHYze97M\nSgA+D+CWzD6/AOBLZrYLAMzsUIP7KCIyJ2kwJiKzYSWA3dH2nuSx2KUAFpB8gORDJN811YFI3k5y\nK8mthw8fPkfdFRGZOxo7ZyyfQ+e8znSbJ5lEVUNURiEzVymeF5arZZbyiXY1+lIMBQsvl/DPI0vx\nE7M9j2LZnjIKnXnpDEZLEtUyr+lkR61GpS2qtUxpi2lKVAC+JAYz+8VzzWrRfhWVtpCzUwBwHYDX\nA2gH8F2S3zOzZ+OdzOwuAHcBwKZNm05jLTQRkfOT5oyJyGzYC2B1tL0qeSy2B0C/mY0CGCX5TQBX\nA3gWIiIXMaUpRWQ2bAGwkeQ6kkUAbwdwT2afLwN4FckCyQ4ANwB4usH9FBGZcxqbpizk0T2/Kzxg\n05e2KLMl7Jbz3XTlKzIpvThRZ7mii+WjsWeO2ZIYURX6bJqScaYkW3YiKhFhvp+0mY9143IWlk03\nxtXysxX4qyd5XpxuPGnlft+XOE0Z7zea141UmZqZVUi+H8DXUC9t8Ukze5Lke5P4nWb2NMl/APAY\n6t9InzCzJ5rXaxGRuUG/XUVkVpjZvQDuzTx2Z2b7wwA+3Mh+iYjMdUpTioiIiDSRBmMiIiIiTdTQ\nNGU+n8e8BQvDA/GSPJm1i+JpTDn6SU35KFps8S+huxCVvSiPudhILZxj1LpdrJprjY5fcbHskkuZ\n4En2yy5BFO2bmZY2XTmJ7HZ2zlitGs2ZO8m8sEolUwIk6njmdKjF89DiOXEFjd1FRERmm367ioiI\niDSRBmMiIiIiTdTQNGVbIY9LF0fpQYvzYz5vxyg1mS17kYuq5xcLvir8kq6w3dvW6WKjlXLa7h/1\nabvxctSXvD8mGVKYpZILwWphX8tnSktkymfEr4L04+CKq6Tv043Valyd3x+zXAkp1WrFp1fdvpm0\naFz2olL1wWpULiROi+7La+wuIiIy2/TbVURERKSJNBgTERERaSINxkRERESaqKFzxrpaC/iRvlDa\nohbNTcqWXqhEc6qq5idqFeLlkOhfQj6apzWv3S+HtLYjtJkpezE2GrYrBf+8+T3z0vbkuB+/VsvR\nsk2Z0g+VTKmJUima35WZ+zVZDq+/VC67WLUatguZcxSL4fy5zJyuoeGRtD04PO5iYFvaLGdKW8Tz\n0OJlmp7IzM8TERGRs6c7YyIiIiJNdMrBGMlPkjxE8onosV6S95HclnxdcG67KSIiInJhmkma8lMA\nPgrg09FjmwHcb2Z3kNycbH/olCcj0dsW0mqwcPpsBf6JakhN7tzxlIvVoqrwq9Ze6WL5YlRJv+bH\nmvF2obXNxdrb29N2DpMuVikfCvu1tLrYvJ7ecMwWn5ZsaWl32xPlkP6sZSvwI07T+uO0FsN2IfOO\n1aKU4sS4T70OjIXnjU3Od7EDh8L5Rsv+fOVKvBpA6KgK8IuIiMy+U/56NbNvAjiaefgWAHcn7bsB\nvHmW+yUiIiJyUTjTex1LzWx/0j4AYOl0O5K8neRWkluHhvrP8HQiIiIiF6azTjxZvUS7nSR+l5lt\nMrNN8+YtnG43ERERkYvSmZa2OEhyuZntJ7kcwKFTPgOAwdwSPTW37I7fN2ehnEN5dNDFHnvi8bR9\nYN8RF7vy6pem7Y72xS5WLoX5T4bskkdhLlt3h5/Q1d0VamKUJ0dcrH9oTzhfa4uLLVq0zG33dHSl\n7WKbn7NWrYT5Xv1HfFb4yMGBtD056eezDQ4cS9vHjh12sdHSRDg+ulxs6bIw166to8f3pRrNbYve\nrxYthyQiIjLrzvS36z0Abk3atwL48ux0R0REROTiMpPSFp8D8F0Al5HcQ/I2AHcAuInkNgBvSLZF\nRERE5DSdMk1pZu+YJvT60z1ZzQxjkyF1Fqcpy+WK27e7GMaJ3Z2dLlaphtTZD5963MXGJkJK74oX\nX+di7a2hHFou51OKbVHJjRynj61b5z+rkItKUpRLvnL+8JAvNXH48L60fezYgIsdOnAgbR885LO+\nk1GJ/JExX0l/KDrH6IiPtXWEVOhVL1nnYsuXLE/bVfqUbZyaNIvLaqgCv4iIyGzTJCARERGRJtJg\nTP5fe/ceI1d5ngH8eWZ2dte79vq2a8dX7OKlYHIhxBCiNCpJmspAU9qqTSBVSlpSlzRXKZWC0ipt\n1f5hFDUtUkIRIQgikCiCKLGAhCIrtElbCIZAiCFgx8HYwRfW9t53Z+fy9o85nu/ivZndPWe8+/wk\na88537l8c2bX++1533k/ERERyZAGYyIiIiIZerOlLd4UM0OxXPLWvdykaHogg9vQuWpd0DYGl8Nl\nuWrQNjzoyjsMnPpl0HbeVpdDtnx5WOphyRJXvmJJe1h2AubKSQycDMtOjAy5PK2e3jAP7MUX9wXr\nPcdc0dtyKcwn83PmSpVwjHy0x5X2yDeH/W5b7Gq3rVp/QdC2ZUt3fXn16jDXrX/QTTdVsTDXrVJx\neXBlb7qlcrkCkYmQ3A7gVgB5AHea2bgf7CF5GWofCrrOzB5MsYsiIg1JT8ZEZMZI5gF8A8BVALYC\nuJ7k1gn2uwXAf6bbQxGRxqXBmIjMhssB7DezA2Y2BuB+1OawjX0WwEOYZqFoEZGFIN0wJcLwox/2\nsmpYgn/QKxMxVgnLXrxxasDt1xvOd9m9+a315a4ly4K24VNuXxsNK+kfO+TCjUPDA0Fb7ylX5f/E\n8TBMefLEUH25ZzCcKQC5sBTE2i5XkX9J+6KgbUu3Kz0xXAqPW9bnwqSr1p0ftLW2ddSXK9UwZFvx\nQoyvnwxnKijk3fuQz4XfBhWvdEip5N6HqoXnF/GsA3DIWz8M4N3+DiTXAfhDAO8HcNlEJyK5A8AO\nANi4ceOsd1REpNHoyZiIpOXfAHzJbPJRvT+fbVdX12S7iojMC6k+GROReevXADZ46+uTbb5tAO4n\nCQCdAK4mWTaz76bTRRGRxqTBmIjMhqcBdJPcjNog7DoAH/N3MLN6LJ7k3QAe1kBMRCTlwVi1UkX/\noJsOaWjYlXeoFMOpfMwrr1AcPRW0dS51eVIXbewM2taudblX+18LS02cOO5KTRTyYY6aeeUdxqpj\nQVsu76K57YvC0hKLO13JiJbOFUHbli1hvsuqFV57OazlsbLTTU80MBqWkFhbba4vnxwMIzxDRZdP\n5k9dBACFnLtGS0tz0NbR1uLacmFfimMu12y07MqI5HOKasv4zKxM8jMAHkOttMVdZraX5E1J++2Z\ndlBEpIHpyZiIzAozexTAo9G2cQdhZvaJNPokInIu0KMOERERkQyl+mRsdKyE/a+5T78XvRIKhagC\nP7yIW7Ucjhk3XnBJfXlFR/gSjg27EOPRgfC4tmXr68uLotISy7wK/Jds3RS0dXS40GQuF4b7Wlpc\nGG9sJKyq39wchhRPvnGkvtwXlcF4/rmf1ZcLbe1B24qVriTGyrYlQVufV/bjyLE3grblXjh37aq1\nQVuHN8tA3sLSIQPDLvTZ593PHOM3SURERGZKT8ZEREREMqTBmIiIiEiGNBgTERERyVCqOWPlSglv\nnHR5TYNeLlhblCc1NuRKXYyVw5ymnDeVT6FnKGh7y0o3BdKW7vOCtuXLXFuhEE45dOLYsfpy30B4\nPX96pHw+umVeMfGO1jCnqtIclpooFd15Fy8Jc9aGxlw5i1dfC3O/zt/opkA6f9OGoK1vwN2nxYWg\nCUuXuFy3ruVtQVuhyd37kWKY29be5vpWLLvXwJxyxkRERGabnoyJiIiIZEiDMREREZEMaTAmIiIi\nkqFUc8baFrXiXW+7sL7+/f9+pr5cqYb5SB1e/hMRth3rd1MqrVndFbRtWOtqib1981uCtoqXwvXL\nA68GbTkv92vfwaNhXzpcva7mQlhnbHTU5ax1nxdOldTXOxisP/PTn3oXDKc8Yt7VD7v0ne8N2to6\nVtaXD78e5pOVyu48nStXBm2dK5bXl61aCtpKJZe/ViyFOWOHjxyvL5/sd7XTSmPhOURERGTm9GRM\nREREJEOam1JERObMppsfyboL56xXd16TdRckJakOxhYVCrhovQsdvrJmVX35eN9osO+Vl11UX25p\nDctA3LvrR/XlwRMngjbb5M65f/+BoG1o1E3tM1YOy074q8OjI0Fbz6DrW6UahvQWtbp46rLOlqDt\n0Gs9wfrhU27f0XIYpizk3DW7jvUGbX1Dbt++3v6grSnvHm4uXxaGSQeL7rhCPgz10twLLpbD13T0\nhLvG0ZNu2qa4xIiIiIjMnMKUIiIiIhmacjBGcgPJH5J8keRekp9Ptq8g+TjJfcnX5VOdS0RERERC\n03kyVgbwRTPbCuAKAJ8muRXAzQB2m1k3gN3JuoiIiIichSlzxszsCIAjyfIAyZcArANwLYArk93u\nAfAEgC9Ndq6KGQZHXHmEjWtd/lhbezitUXm0WF9etawjaNt6XqfXv/AaTU1umqMjJ8L8qpNevlVz\nlIdW9U40Vg5LOIyMuPIO8fWWLnUlKZ7beyho6+8Pc8+qhRX15ZbWcDqmAl1+1ysHDgdtra1uKqNK\nJczvqlZcHlf+UNi5Ra0uh62jI8wnGxtz+XPl6EUVvbIXvf2uPEc5uraIiIjM3FnljJHcBOCdAJ4C\nsDoZqAHAUQCrJzhmB8k9JPcM9p2cQVdFRERE5p9pD8ZILgbwEIAvmFnwyMnMDICNd5yZ3WFm28xs\n2+KlK8bbRURERGTBmlZpC5IF1AZi95nZd5LNx0iuMbMjJNcAOD7xGWrKFUPPkAuPjRRdGK+JYWiw\nSjdObFsShtguuXhLfflUXxiKHCl65Suiqv6Fltb68tBgWB2/tc2FLfP58Lb4pTXKpbC8Q7Ho+r20\nfVnQ1rYoHOue6Btw5xkNx65ehQqMRENkDrn7lG8Kw5vmhQ4tCq/m817I9o3wqeSoF6asRNereuU7\nKhUXPi2ptIWIiMism86nKQngWwBeMrOveU27ANyQLN8A4Huz3z0RERGR+W06T8beC+DjAF4g+Vyy\n7csAdgJ4gOSNAA4C+MjcdFFERERk/prOpyl/DEQzdTsfnN3uiIiIiCwsqU6HZNUqRoddrtaSdpeL\n1dpcCPYdHXF5UocOvR6eB37eVBhpLZddSYxcNIRk8BmDMGer4OVXVRm2jXnTCvnTDwHA8mVL68ub\nz9sQtPX0hNMh9fa7nLGxSpjfVfJys6pR36peXlhLazjl0tL29vpyc0tz0GZ+uQ4vlw4Act7NYfSa\n/FIX5arKWcj0kNwO4FYAeQB3mtnOqP1PUSt/QwADAD5lZs+n3lERkQaj6ZBEZMZI5gF8A8BVALYC\nuD4pDu37FYDfNrO3AfgnAHek20sRkcakwZiIzIbLAew3swNmNgbgftQKQ9eZ2f+a2alk9UkA61Pu\no4hIQ0o1TAkYUHXhuLwXKmtqDUNs9CrL957qC9oq5saQI2Nh+K1UcmG1QhR+Q7N7uTmEFfhzcMe1\ntoShwNLoqDtnIQynthbcOQf6w36WS8VgffEid97m5vD1jnphy5HiaNBWLrm2clRewi9DgVxY9sKX\ni+5FE12/mwrhtwGbXT/HCq6f+ZzG7jKhdQD8KSgOA3j3JPvfCOD74zWQ3AFgBwBs3LhxtvonItKw\n9NtVRFJF8v2oDcbGnT7NLxTd1dWVbudERDKQ8pMxEZmnfg3A/wTL+mRbgOTbAdwJ4CozO5FS30RE\nGpqejInIbHgaQDfJzSSbAVyHWmHoOpIbAXwHwMfN7JUM+igi0pBSfzLmj/5y9MorRGUozJsOqWrh\nmLFaMW85PI5eSTQiLMvQUnA5VYV8KyZSiMpsVBe3eecMNXl5b1YJ87loYYmKpib3OpoKYX5XC1ye\nVmuUT1ZqddePO9Dk5YnlopwuP5/MnxoJAKp+36LqFeZtyI0746hIyMzKJD8D4DHUSlvcZWZ7Sd6U\ntN8O4CsAVgK4rTaxB8pmti2rPouINAqFKUVkVpjZowAejbbd7i1/EsAn0+6XiEijU5hSREREJEPp\nPhmzWhX+0/zQZKUSxsrKJRfyiyNlk0fOXCs58Z5xlYaK169yOayO35SfaDaosMo/o57FJTIKTe52\nl6ISFU10YUTmw7dlUZMXNo1Cn/Q6EM84UC67fXNR2Yu8V+qiGlXZ98PAU91tERERmRk9GRMRERHJ\nkAZjIiIiIhnSYExEREQkQ+nmjDEsWVHycrNKpWiaH/PymKK6F+bnTTHOofLzpMKxpn9cpRzWxCiX\n3LRK1bHwnP55WlvDkhh+1/qHRoK2OBcrOC5a9/PS4lmc/JdrUc6Yefcpagpz2KILBmVFokZ671Eu\n1+Rtnzh3TkRERN4cPRkTERERyZAGYyIiIiIZSjVMaWYoV1x4sOyFCs8I6TGoGRE2eRtyUT0H7/Rn\nhPT8cGNLVHaiySs7USwWo664axQKYXV+/5xnhhDDdf+a+bi2xmSzEXinqVQqUZtfyiM8rlrluPvF\n/FBk7TzuOP96ClOKiIjMPj0ZExEREcmQBmMiIiIiGdJgTERERCRDKeeMhSUl/DymfD6crsfPcGJu\n4tIWk+VCTZ4nFY5D/evHuVF+Pluc28Yg1ysuETHxelOUe+afN84L84+L+x2Kynx4x8X9Dqefmvi4\n4L6cUZBDREREZkpPxkREREQypMGYiIiISIbSrcCPOMToQmVkHKb0y85P//xBGG+SGhGThRDjkKnf\nFoc+S6XShG3xenzeifaNQ5GThSbDCvzh9SYLr052fyfsi6KUIiIis05PxkREREQyNOVgjGQryZ+Q\nfJ7kXpL/mGxfQfJxkvuSr8vnvrsiIiIi88t0nowVAXzAzN4B4BIA20leAeBmALvNrBvA7mRdRERE\nRM7ClDljVksgGkxWC8k/A3AtgCuT7fcAeALAl6Y4F8rlsrful3MIc5qqmCSHapIcrqDURNyBScpQ\n+OLcLv/6cdmJM6Zx8kxWWqNpkvwxf2qmqc5pNln5iomnmwpmm2IuapvefRIREZGZm1bOGMk8yecA\nHAfwuJk9BWC1mR1JdjkKYPUc9VFERERk3prWYMzMKmZ2CYD1AC4n+dao3TDBZx5J7iC5h+SekaHe\nGXdYREREZD45q9IWZtZL8ocAtgM4RnKNmR0huQa1p2bjHXMHgDsAYPW6C2ys7EpB+IX1zxjJVb0w\nZVMUKst7FenjYKR3oskCbIwuyMkq23snykVhOz8UGF8vDg2W/bBhtG9usvCqJ75P5vctinx6UWBU\noza/mj4tKvPhL/vlQCbpl4iIiLw5Uw7GSHYBKCUDsUUAPgTgFgC7ANwAYGfy9Xtz2VERaWwktwO4\nFUAewJ1mtjNqZ9J+NYBhAJ8ws2dT76jIArXp5key7sI569Wd18zp+afzZGwNgHtYq8qaA/CAmT1M\n8v8APEDyRgAHAXxkDvspIg0s+f/hG6j9sXYYwNMkd5nZi95uVwHoTv69G8C/J19FRBa06Xya8mcA\n3jnO9hMAPjgXnRKRc87lAPab2QEAIHk/ap+49gdj1wL4dpJj+iTJZadTHdLvrohI40h1OqTjr+/r\nufVvtx8E0AmgJ81rnyMa/b6cl3UHpGGtA3DIWz+MM596jbfPOgDBYIzkDgA7ktJ9WjgAAAWLSURB\nVNVBki/PblcbQsP+rPOWrHuQqoZ9HwC9F41kBu/FtH5vpjoYM7MuACC5x8y2pXntc4Hui0j4oZ/5\nSj/rjUHvQ+NY6O+F5qYUkdnwawAbvPX1ybaz3UdEZMHRYExEZsPTALpJbibZDOA61D5x7dsF4M9Y\ncwWAPuWLiYikHKb0zOsQxAzovsg5yczKJD8D4DHUSlvcZWZ7Sd6UtN8O4FHUylrsR620xZ9n1d8G\noJ/1xqD3oXEs6PeCk82fKCIiIiJzS2FKERERkQxpMCYiIiKSoVQHYyS3k3yZ5H6SN6d57UZCcgPJ\nH5J8keRekp9Ptq8g+TjJfcnX5Vn3VUQmRvJRksvO8pi7Sf7xXPVJJkbyVZKd42z//YX8O2mukdxE\n8ufjbL+T5NZpHH8lyYfnpneNIbXBmDddylUAtgK4fjpvwjxVBvBFM9sK4AoAn07uxc0AdptZN4Dd\nybqINCgzu9rMev1tyadFFXU4h5jZrnguVZl7ZvbJaMo0APXxwoKS5n8Y9elSzGwMwOnpUhYcMzty\neoJkMxsA8BJqlcivBXBPsts9AP4gmx6KSIzkd0k+kzzN3pFse5VkZ/KX/8skvw3g5wA2kBwk+a/J\n/rtJdo1zzq+QfJrkz0nekUymDpJPkLyF5E9IvkLyfcn2PMmvJsf8jORfpXkPziUk20k+QvL55P5+\nNGn6LMlnSb5A8sJk30+Q/HqyfDfJ20nuSe7972X2IuaXJpL3kXyJ5IMk25Lv820AkPy8/AvJ5wG8\nJ4mk/YLkswD+KNuuz700B2MTTYWyoJHchNrcn08BWO3VXToKYHVG3RKRM/2Fmb0LwDYAnyO5Mmrv\nBnCbmV1sZgcBtAPYY2YXA/gvAH8/zjm/bmaXmdlbASwC4P/ibzKzywF8wTv2RtTqs10G4DIAf0ly\n82y9wHlmO4DXzewdyf39QbK9x8wuRW2i+r+Z4NhNqD1AuAbA7SRb57qzC8BvovbzcRGAfgB/HbW3\nA3jKzN4BYA+AbwL4MIB3AXhLmh3Ngh6lZ4jkYgAPAfiCmfX7bclkyqo7ItI4Ppf81f4kajMJdEft\nB83sSW+9CuA/kuV7AfzWOOd8P8mnSL4A4AMALvbavpN8fQa1wQEA/C5qhXOfQ+0PuJXj9ENqXgDw\noeQJ4/vMrC/ZPt59jT1gZlUz2wfgAIAL57arC8IhM/ufZHm8n4cKar8Pgdr9/pWZ7Ut+F96bUh8z\nk2bRV02F4iFZQO0b7z4zO/2fwzGSa8zsCMk1AI5n10MROY3klQB+B8B7zGyY5BMA4qclQ1OcJvjj\nKnnachuAbWZ2iOQ/ROcsJl8rcP9XE8Bnzeyxs30NC42ZvULyUtQKDf8zyd1J03j39YzDp1iXszfV\nPR01s0panWk0aT4Zm850KQtCkhfyLQAvmdnXvKZdAG5Ilm8A8L20+yYi41oK4FQyELsQtQ/eTCUH\n4PSnJj8G4MdR++mBV0/ylHw6n7B8DMCnkj/mQPICku3TOG7BIbkWwLCZ3QvgqwAuPYvD/4RkjuT5\nAH4DwMtz0ccFZiPJ9yTL4/08+H4BYFNy/wHg+jntWQNI7cnYRNOlpHX9BvNeAB8H8EISbgCALwPY\nCeABkjcCOAjgIxn1T0RCPwBwE8mXUPvF/OQU+wO1J2WXk/w71J5yf9RvNLNekt9ELeH/KGp/sE7l\nTtRCa88mf9S9AX3QZyJvA/BVklUAJQCfAvDgNI99DcBPAHQAuMnMRuemiwvKy6hVDrgLwIuo5ex9\neLwdzWw0+ZDMIySHAfwIwJLUepoBTYckIjIHSA6a2eKs+yFnh+TdAB42s+kO3ERmTAn8IiIiIhnS\nkzERERGRDOnJmIiIiEiGNBgTERERyZAGYyIiIiIZ0mBMREREJEMajImIiIhk6P8BjAL4pNPCejYA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f908ef69400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "idx = np.random.choice(len(test_x),5,replace=False)\n",
    "\n",
    "\n",
    "p = model.predict(test_x[idx])\n",
    "for i in range(len(idx)):\n",
    "    plt.subplot(5,2,2*i+1)\n",
    "    plt.imshow(test_x[idx[i]])\n",
    "    plt.title(label_dict[test_y[idx[i]]])\n",
    "#     plt.show()\n",
    "    pred_label = np.argsort(-p[i])[:3]\n",
    "    pred_prob = [p[i][l] for l in pred_label]\n",
    "    pred_label = [label_dict[l] for l in pred_label]\n",
    "    \n",
    "    plt.subplot(5,2,2*i+2)\n",
    "    plt.bar(range(3),pred_prob)\n",
    "    plt.xticks(range(3), pred_label)\n",
    "#     plt.show()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization\n",
    "\n",
    "Batch Normalization makes the output of multiplying by weights 0 mean and variance of one **before** passing through an activation layer. This makes sure that the gradients are neither large or too small. Making the learning process faster. \n",
    "\n",
    "https://www.quora.com/Why-does-batch-normalization-help"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(8, kernel_size=(3,3), padding='same', input_shape = (width,height,channels)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(16, kernel_size=(3,3), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(32, kernel_size=(3,3), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "model.compile(optimizer='adadelta', loss='sparse_categorical_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_4 (Conv2D)            (None, 32, 32, 8)         224       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 32, 32, 8)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 16, 16, 16)        1168      \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 16, 16, 16)        64        \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 16, 16, 16)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 16)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 8, 8, 32)          4640      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 8, 8, 32)          128       \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 11,354.0\n",
      "Trainable params: 11,258.0\n",
      "Non-trainable params: 96.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "195/195 [==============================] - 45s - loss: 1.9342    \n",
      "Epoch 2/15\n",
      "195/195 [==============================] - 51s - loss: 1.5972    \n",
      "Epoch 3/15\n",
      "195/195 [==============================] - 51s - loss: 1.4710    \n",
      "Epoch 4/15\n",
      "195/195 [==============================] - 50s - loss: 1.3979    \n",
      "Epoch 5/15\n",
      "195/195 [==============================] - 50s - loss: 1.3430    \n",
      "Epoch 6/15\n",
      "195/195 [==============================] - 50s - loss: 1.2985    \n",
      "Epoch 7/15\n",
      "195/195 [==============================] - 50s - loss: 1.2603    \n",
      "Epoch 8/15\n",
      "195/195 [==============================] - 50s - loss: 1.2246    \n",
      "Epoch 9/15\n",
      "195/195 [==============================] - 48s - loss: 1.1945    \n",
      "Epoch 10/15\n",
      "195/195 [==============================] - 51s - loss: 1.1650    \n",
      "Epoch 11/15\n",
      "195/195 [==============================] - 56s - loss: 1.1407    \n",
      "Epoch 12/15\n",
      "195/195 [==============================] - 48s - loss: 1.1179    \n",
      "Epoch 13/15\n",
      "195/195 [==============================] - 53s - loss: 1.0988    \n",
      "Epoch 14/15\n",
      "195/195 [==============================] - 47s - loss: 1.0793    \n",
      "Epoch 15/15\n",
      "195/195 [==============================] - 47s - loss: 1.0628    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f908e03b278>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 256\n",
    "model.fit_generator(get_batch(batch_size=batch_size), train_examples//batch_size, epochs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9888/10000 [============================>.] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5948"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict_classes(test_x)\n",
    "np.count_nonzero(y_pred == test_y)/len(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9PtJoT0z46cVMVEojl8+52MSYnxoslkNfy2VfnX/v7l2Ndm/fChcbGR4O71nx06TFa3+x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\n/wfGQ9qvmRAjKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f907d093400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "idx = np.random.choice(len(test_x),5,replace=False)\n",
    "\n",
    "\n",
    "p = model.predict(test_x[idx])\n",
    "for i in range(len(idx)):\n",
    "    plt.subplot(5,2,2*i+1)\n",
    "    plt.imshow(test_x[idx[i]])\n",
    "    plt.title(label_dict[test_y[idx[i]]])\n",
    "#     plt.show()\n",
    "    pred_label = np.argsort(-p[i])[:3]\n",
    "    pred_prob = [p[i][l] for l in pred_label]\n",
    "    pred_label = [label_dict[l] for l in pred_label]\n",
    "    \n",
    "    plt.subplot(5,2,2*i+2)\n",
    "    plt.bar(range(3),pred_prob)\n",
    "    plt.xticks(range(3), pred_label)\n",
    "#     plt.show()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
